Deep convolutional neural network model based chemical process fault diagnosis Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-11 Hao Wu, Jinsong Zhao
Numerous accidents in chemical processes have caused emergency shutdowns, property losses, casualties and/or environmental disruptions in the chemical process industry. Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences. However, FDD is still far from widely practical applications. Over the past few years, deep convolutional neural network (DCNN) has shown excellent performance on machine-learning tasks. In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis. The benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance of the fault diagnosis method.
A sustainable process design to produce diethyl oxalate considering NOx elimination Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-10 Jiaxing Zhu, Lin Hao, Yaozhou S un, Bo Zhang, Wenshuai Bai, Hongyuan Wei
Diethyl oxalate (DEO) is widely used in fine chemical industry. In comparison with traditional esterification process, carbon monoxide coupling process is a novel routine for DEO production. This environmentally friendly process provides better selectivity and yield. Its unique feature is that a closed regeneration-coupling circulation is formed. Toxic byproduct-nitric oxide (NO) from coupling reaction is recycled to re-produce ethyl nitrite through regeneration reaction. This avoids significant amount of NOx emission. However, due to a few NOx emission, a contaminant handling system is applied for environmental protection. A systematical environmental analysis is also carried out to assess this process. Regeneration-coupling circulation brings interaction behaviors and some trade-offs including reactor size and recycle flowrate, regeneration and coupling reaction, loss of reactants and NO emission. Thus, a rigorous steady simulation is established to investigate these trade-offs. Then DEO process is optimized to obtain the optimal design. Finally a more economic flowsheet to produce DEO is proposed.
Optimizing the Design of New and Existing Supply Chains at Dow AgroSciences Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-07 Matt Bassett
In this paper, we discuss the design and optimization of supply chains at Dow AgroSciences. We start by introducing the design components of a typical supply chain. We then discuss the typical inputs required in a model and the type of outputs generated. Next we consider the strengths and weaknesses of the standard tools that we use as part of the model process.To show the breadth of problems addressed, three example models are presented. The first problem discusses the complexity of addressing a global supply chain for a new active ingredient. The second problem focused on a regional supply chain. The final problem showed a tactical model looking at rail fleet sizing.
A Methodology to Restructure a Pipeline System for an Oilfield in the Mid to Late Stages of Development Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-06 Bohong Wang, Yongtu Liang, Jianqin Zheng, Tiantian Lei, Meng Yuan, Haoran Zhang
One important issue in the mid to late development stages of oilfields is maintaining stable production, especially when the existing gathering pipeline system cannot fully satisfy the development of low pressures and low production rates. In these cases, it is necessary to restructure the original gathering pipeline system. In this study, an optimal design method is proposed to restructure a pipeline system in an oilfield in the mid to late development stages. Based on the demand of stable production and the existing structure of the pipeline system, a mixed-integer nonlinear programming (MINLP) model with an objective function that minimizes the total cost is developed. Hydraulic, technical and economic constraints are considered. The model is linearized based on a piecewise method and solved by the branch-and-bound algorithm. This method is applied to a real case study of a pipeline system in an oilfield.
Quality-relevant independent component regression model for virtual sensing application Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-06 Xinmin Zhang, Manabu Kano, Yuan Li
Independent component regression (ICR) is an efficient method for tackling non-Gaussian problems. In this work, the defects of the conventional ICR are analyzed, and a novel quality-relevant independent component regression (QR-ICR) method based on distance covariance and distance correlation is proposed. QR-ICR extracts independent components (ICs) using a quality-relevant independent component analysis (QR-ICA) algorithm, which simultaneously maximizes the non-Gaussianity of ICs and statistical dependency between ICs and quality variables. Meanwhile, two new types of statistical criteria, called cumulative percent relevance (CPR) and Max-Dependency (Max-Dep), are proposed to rank the order and determine the number of ICs according to their contributions to quality variables. The proposed QR-ICR(CPR) and QR-ICR(Max-Dep) methods were validated through a vinyl acetate monomer production process and a benchmark near-infrared spectral data. The results have demonstrated that the proposed QR-ICR(CPR) and QR-ICR(Max-Dep) provide simpler predictive models and give better prediction performances than PLS, ICR, ICR(CPR), and ICR(Max-Dep).
A decision support platform for a bio‐based supply chain: Application to the region of Lower Saxony and Bremen (Germany). Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-06 Christos Galanopoulos, Diego Barletta, Edwin Zondervan
In this work, a biomass supply chain model, for the region of Lower Saxony and Bremen in northern Germany, has been developed. Because of Germany's high demand for biofuels, the production and distribution of levulinic acid and bioethanol by wheat straw is studied. An illustrative bio-based supply chain model is developed and implemented in the Advanced Interactive Multidimensional Modeling (AIMMS) software. Then, this model is used to study the logistics, network optimization, transportation and inventory management, and the resulting environmental and economic impacts. In the end, a sensitivity analysis is conducted to evaluate the influence of key model parameters on these impacts. The results showed that a wheat straw supply chain network is profitable in the area of Bremen and Lower Saxony even though the bioproducts demand is not fully covered and that the transportation costs did not have a strong impact on the supply chain network.
Application of Neural Networks for Optimal-Setpoint Design and MPC Control in Biological Wastewater Treatment Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-05 Mahsa Sadeghassadi, Chris.J.B. Macnab, Bhushan Gopaluni, David Westwick
This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the influent changes are periodic and thus predictable. Specifically, a nonlinear optimization procedure utilizes dry weather data to decide on a nominal fixed setpoint, or a weighting gain, or both; during weather events an algorithm uses the optimization solution(s) together with the ammonium predictions to adjust the setpoint dynamically (responding appropriately to significant changes in the influent). A constrained nonlinear neural-network model predictive control tracks the setpoint. Simulations with the BSM1 compare several variations of the proposed methods to a fixed-setpoint PI control, demonstrating improvement in effluent quality or reduction in energy use, or both.
Reactive Scheduling of Crude Oil using Structure Adapted Genetic Algorithm under Multiple Uncertainties Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-03 Debashish Panda, Manojkumar Ramteke
A CFD simulation study of boiling mechanism and BOG generation in a full-scale LNG storage tank Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-03 Abdullah Saleem, Shamsuzzaman Farooq, Iftekhar A. Karimi, Raja Banerjee
Despite heavy insulation, the unavoidable heat leak from the surroundings into an LNG (Liquefied Natural Gas) storage tank causes boil-off-gas (BOG) generation. A comprehensive dynamic CFD simulation of an onshore full-scale LNG tank in a regasification terminal is presented. LNG is approximated as pure methane, the axisymmetric VOF (Volume of Fluid) model is used to track the vapor-liquid interface, and the Lee model is employed to account for the phase change including the effect of static pressure. An extensive investigation of the heat ingress magnitude, internal flow dynamics, and convective heat transfer gives useful insights on the boiling phenomena and a reliable quantification of the BOG. Surface evaporation is the governing boiling mechanism and nucleate boiling is unlikely with proper insulation. The critical wall superheat marking the transition from surface evaporation to nucleate boiling is estimated as 2.5-2.8 K for LNG.
Optimal Synthesis of Periodic Sorption Enhanced Reaction Processes with Application to Hydrogen Production Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-03 Akhil Arora, Ishan Bajaj, Shachit S. Iyer, M. M. Faruque Hasan
Optimization-based approach for maximizing profitability of bioethanol supply chain in Brazil Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-03 Andrei Kostin, Diogo H. Macowski, Juliana M.T.A. Pietrobelli, Gonzalo Guillén-Gosálbez, Laureano Jiménez, Mauro A.S.S. Ravagnani
In this work, a mathematical approach for optimizing and planning the Brazilian bioethanol supply chains (SC) is presented. The optimization problem has an MILP formulation, aiming to maximize the net present value (NPV) of the entire SC of the sugar and bioethanol sector in Brazil. The model takes into account seven different production technologies, two types of warehouses, three types of transportation modes and seven exportation options, whose data were obtained from Brazilian industrial practices. The model aims to propose the optimal configuration of a bioethanol network, that is, the locations of the production and storage facilities, their capacity of expansion policy, the technology selected for manufacturing and materials storage and the flows of all feedstock and final products involved in the bioethanol SC in Brazil. A comparison between the current situation of Brazilian bioethanol SC and the optimal configuration achieved by the proposed model is also included.
Computer aided chemical product design - ProCAPD & tailor-made blended products Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-04-01 Sawitree Kalakul, Lei Zhang, Zhou Fang, Hanif A Choudhury, Saad Intikhab, Nimir Elbashir, Mario R. Eden, Rafiqul Gani
In chemical product design, application of computer-aided methods helps to design as well as improve products to reach the market faster by reducing time-consuming experiments at the early stages of design. That is, experiments are performed during the later stages as a verification or product refinement step. Computer-aided molecular and mixture-blend design methods are finding increasing use because of their potential to quickly generate and evaluate thousands of candidate products; to estimate a large number of the needed physico-chemical properties; and to select a small number of feasible product candidates for further verification and refinement by experiments. In this paper, an extended computer-aided framework and its implementation in a product design software tool is presented, highlighting the new features together with an overview on the current state of the art in computer-aided chemical product design. Results from case studies involving tailor-made blend design are presented to highlight the latest developments.
Distributed fault diagnosis for networked nonlinear uncertain systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-30 Hadi Shahnazari, Prashant Mhaskar
In this work, we address the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems utilizing a distributed fault detection and isolation (FDI) strategy. The key idea is to design a bank of local FDI (LFDI) schemes that communicate with each other for improved FDI. The proposed distributed FDI scheme is shown to be able to handle local faults as well as those that affect more than one subsystem. This is achieved via appropriate adaptation of the LFDI filters based on information exchange with other subsystems and using the proposed notion of detectability index. The detectability index and isolability conditions are rigorously derived for the distributed FDI scheme. Effectiveness of the proposed methodology is shown via application to a reactor-separator process subject to uncertainty and measurement noise.
Errata: Heat Exchanger Network Cleaning Scheduling: From Optimal Control to Mixed-Integer Decision Making Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-30 Sai Darshan Adloor, Riham Al Ismaili, Vassilios S. Vassiliadis
Errata to the article by [Al Ismaili, R., Lee, M., Wilson, D., Vassiliadis, V., 2018. Heat exchanger network cleaning scheduling: From optimal control to mixed-integer decision making. Computers & Chemical Engineering 111, 1–15.] on the optimal scheduling of cleaning actions for Heat Exchanger Networks subject to fouling are presented. Errors present in the equations of the Pontryagin Minimum Principle analysis of the original article are indicated and rectified. It is noted that despite these errors, there is no change to the conclusions of the analysis given in [Al Ismaili, R., Lee, M., Wilson, D., Vassiliadis, V., 2018. Heat exchanger network cleaning scheduling: From optimal control to mixed-integer decision making. Computers & Chemical Engineering 111, 1–15.].
Steady-State Real-Time Optimization using Transient Measurements ☆ Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-29 Dinesh Krishnamoorthy, Bjarne Foss, Sigurd Skogestad
Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a “hybrid” approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations.
A Methodology to Reduce the Computational Cost of Transient Multiphysics Simulations for Waste Vitrification Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-29 Alexander W. Abboud, Donna Post Guillen
Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-28 Melis Onel, Chris A. Kieslich, Yannis A. Guzman, Christodoulos A. Floudas, Efstratios N. Pistikopoulos
This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the pre-aligned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
Optimization of Dimethyl Ether Production Process Based on Sustainability Criteria Using a Homotopy Continuation Method Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-28 Javad Asadi, Farhang Jalali Farahani
Traditional criteria for designing processes that utilize only economic aspects can have a negative impact on the environment and society. In this study, a process for the production of dimethyl ether (DME) from methanol is evaluated by employing sustainability metrics. Operational conditions are optimized by implementing a rigorous global multi-objective optimization algorithm based on maximization of economic performance measured by the return on investment (ROI) and minimization of environmental and social impacts. The most efficient operational conditions for DME production based on sustainability criteria are obtained with a homotopy continuation method in conjunction with a process simulator. The resulting conditions indicate that the global warming metric of the DME process is decreased more than 10 times and the decrease of photochemical smog formation, mass intensity and energy intensity is 96%, 12% and 9% respectively. The optimized, sustainable process shows an only an insignificant reduction in terms of economic aspect.
A multistream heat exchanger model with enthalpy feasibility Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-27 Kyungjae Tak, Hweeung Kwon, Jaedeuk Park, Jae Hyun Cho, Il Moon
A temperature feasibility constraint is an important part of multistream heat exchanger (MSHE) modeling. However, temperature feasibility of an MSHE model makes a numerical issue when a physical property package is used to obtain highly accurate temperature-enthalpy relationships in equation-oriented modeling environment. To resolve the issue, this study propose a new MSHE model with enthalpy feasibility using the fact that enthalpy is a monotonically increasing function of temperature. A natural gas liquefaction process, called a single mixed refrigeration process, is optimized using the proposed MSHE model under an equation-oriented modeling environment with a physical property package as a case study.
Modeling and solution for steelmaking scheduling with batching decisions and energy constraints ☆ Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-26 Wenjie Xu, Lixin Tang, Efstratios N. Pistikopoulos
This paper investigates a practical steelmaking scheduling problem with batching decisions and energy constraints, in which batching is used to decide how to group and sequence a number of jobs to form job groups so as to meet batch production mode. Incorporating energy consideration into the scheduling is motivated by practical demand and the potential to reduce the energy bill through optimal scheduling. Based upon our proposed energy expressions, an MINLP model is formulated and solved using the spatial branch and bound (B&B) algorithm, which is enhanced by the proposed decomposition strategy working as a node heuristic. The benefits of the integrated scheduling and some sensitivity analysis experiments are reported on an illustrative example. Experiments on randomly generated instances show that the energy expressions are superior to Hadera et al.(2015), the B&B outperforms the state-of-the-art commercial solvers, and the decomposition strategy performs well running as an independent heuristic.
Fault detection and diagnosis using empirical mode decomposition based principal component analysis Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-24 Yuncheng Du, Dongping Du
This paper presents a new algorithm to identify and diagnose stochastic faults in Tennessee Eastman (TE) process. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) to diagnose a group of faults that could not be properly detected and/or diagnosed with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, fault detection, and fault diagnosis. Measured variables are first decomposed into different scales using the EEMD-based PCA, from which fault signatures can be extracted for fault detection and diagnosis (FDD). The T2 and Q statistics-based CUSUMs are further applied to improve fault detection, where a set of PCA models are developed from historical data to characterize anomalous fingerprints that are correlated with each fault for accurate fault diagnosis. The algorithm developed in this paper can successfully identify and diagnose both individual and simultaneous occurrences of stochastic faults.
Process modelling, design and technoeconomic evaluation for continuous paracetamol crystallisation Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-22 Hikaru Jolliffe, Dimitrios I. Gerogiorgis
Continuous Pharmaceutical Manufacturing (CPM) has a strong potential to catalyse pharmaceutical innovation. This paper analyses Continuous Oscillatory Baffled Crystalliser (COBC) optimal design and performance for paracetamol crystallisation, via systematic modelling and nonlinear optimization (NLP). Clear trends emerge, with rate of antisolvent use having a marked impact of COBC volumes; crystal seed mass loading also has a strong effect. For the base case studied (inlet temperature of 50°C, seed crystal size of 40 microns) the optimal solution was for 2% seed mass loading (with respect to solute mass) and with 80% water antisolvent use (by mass with respect to process solvent acetone); the crystalliser size was 4.25 L with a total cost of 101,370 GBP, achieving a product yield of 50% with a product crystal size of 83.6 microns. Clear tradeoffs among mass efficiency, volume, cost and product crystal size have been illustrated, providing valuable quantitative insights into process performance.
Chromatography Analysis and Design Toolkit (CADET) Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-20 Samuel Leweke, Eric von Lieres
CADET is an open source modeling and simulation framework for column liquid chromatography. The software is freely distributed to both academia and industry under the GPL license (http://github.com/modsim/cadet). CADET is based on a core simulator that is written in object oriented C++ and applies modern mathematical algorithms for efficiently solving a variety of customary chromatography models. This simulation engine is interfaced to a suite of MATLAB tools for setting up and executing the most common scientific workflows, e.g., model calibration, process design, robustness analysis, statistical analysis and experimental design. The model library and numerical methods are continuously extended and improved. For instance, binding models with multiple bound states, pH and/or temerature dependence of binding parameters, surface diffusion and arbitry spacing of the radial discretization have been recently added. Moreover, numerical accuracy and computational speed of the code are comprehensively benchmarked using high precision reference solutions and realistic model problems. Versatility of the CADET modeling platform is demonstrated with several examples that are also published as open source code and can be freely adapted to specific use cases. In one of several case studies, sequential and simultaneous optimization of elution gradient shape and cut times are compared for a three component separation. This process is designed to achieve Pareto optimal purity and yield of the central fraction. Moreover, the robustness of these designs with respect to typical process variations is systematically studied. The last case study illustrates the optimal design of experiments for estimating model parameters with maximal accuracy.
In Situ Adaptive Tabulation (ISAT) for combustion chemistry in a network of perfectly stirred reactors (PSRs) for the investigation of soot formation and growth Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-20 Sudip Adhikari, Alan Sayre, Abhilash J. Chandy
This paper presents an efficient computational implementation of the in situ adaptive tabulation (ISAT) approach (Pope, 1997) for combustion chemistry in a network of perfectly stirred reactors (PSRs) for the investigation of soot formation and growth. This study, for the first time, extends the thermochemical composition vector to contain the soot moments, using the method of moments with interpolative closure (MOMIC) as a soot model with six concentration moments. A series of PSR calculations is carried out using the direct integration (DI) and ISAT approaches. Assessment of the accuracy of ISAT approach is conducted through direct comparisons with DI calculations. Moreover, complimentary cumulative distribution function (CCDF), sensitivity of ISAT calculations with respect to the absolute error tolerance values and speedup are analyzed for two different test cases of ethylene-air using two different chemical kinetic mechanisms. A maximum speedup of 50x was achieved for an error tolerance of 10−4 10 − 4 .
Large-Scale DAE-Constrained Optimization Applied to a Modified Spouted Bed Reactor for Ethylene Production from Methane Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 D.M. Yancy-Caballero, L.T. Biegler, R. Guirardello
In this paper, a modified spouted bed reactor is proposed to enhance the yield of the oxidative coupling of methane (OCM). Optimization techniques are used to carry out a theoretical analysis of ethylene production via OCM and define some optimal operating conditions of the reacting system. A model-based DAE-constrained optimization strategy is proposed and applied to the OCM process to illustrate the computational capability of the proposed formulation, and the theoretical feasibility of the proposed reactor. The model developed for the reactor is a one-dimensional model composed of material, energy, and momentum balances. This model along with the kinetic model constitute a non-linear and differential-algebraic system, which is discretized using orthogonal collocation on finite elements with continuous profiles approximated by Lagrange polynomials. The resulting algebraic collocation equations are written as equality constraints in the optimization problem, which is solved with the IPOPT solver within the optimization-modeling platform. An initialization routine based on simulations was carried out to guarantee convergence in optimizations. Results from simulations and optimizations showed the potential of combining different reactor concepts to improve the ethylene production from natural gas via oxidative coupling of methane.
Simulation of Hybrid Trickle Bed Reactor-Reverse Osmosis Process for the Removal of Phenol from Wastewater Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 M.A. Al-Obaidi, A.T. Jarullah, C Kara-Zaïtri, I.M Mujtaba
Phenol and phenolic derivatives found in different industrial effluents are highly toxic and extremely harmful to human and the aquatic ecosystem. In the past, trickle bed reactor (TBR), reverse osmosis (RO) and other processes have been used to remove phenol from wastewater. However, each of these technologies has limitations in terms of the phenol concentration in the feed water and the efficiency of phenol rejection rate. In this work, an integrated hybrid TBR-RO process for removing high concentration phenol from wastewater is suggested and model-based simulation of the process is presented to evaluate the performance of the process. The models for both TBR and RO processes were independently validated against experimental data from the literature before coupling together to make the hybrid process. The results clearly show that the combined process significantly improves the rejection rate of phenol compared to that obtained via the individual processes.
Life Cycle Aggregated Sustainability Index for the Prioritization of Industrial Systems Under Data Uncertainties Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 Jingzheng Ren
Decoupling the constraints for process simulation in large-scale flowsheet optimization Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 Yoshikazu Ishii, Fred D. Otto
A distinct advantage of sequential quadratic programming (SQP) is global convergence that ensures convergence from a remote starting point. When the constraints are highly nonlinear such as in flowsheet optimization, however, locally convergent Newton's method used in SQP as the equation-solving tool may deteriorate the behavior of convergence. Our recognition that this issue remains to be resolved motivated us to study a two-tier SQP approach where the constraints for process simulation consisting of nonlinear equations are decoupled from the KKT system in order to block the adverse influence of nonlinearity on global convergence. Our equation oriented (EO) process simulator (Ishii & Otto, 2011) is employed to decouple the constraints and for maintaining feasibility of the decoupled constraints. The effectiveness and potential of the two-tier SQP approach for reliably and efficiently solving large-scale flowsheet optimization problems are numerically illustrated with fully thermally coupled distillation problems.
CFD-Aspen Plus interconnection method. Improving thermodynamic modelling in computational fluid dynamic simulations Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 Luis Vaquerizo, María José Cocero
Thermodynamic modelling in CFD is basically limited to the models available in the simulators. The method presented in this paper connects CFD simulators with Aspen Plus which instantaneously calculates and returns the value of any physical property required. Therefore, all the thermodynamic models and compounds available in Aspen Plus can be implemented in CFD simulations. The connection, created via Matlab and Excel-VBA, has been validated solving two identical CFD simulations first selecting a thermodynamic model available in the simulator and then connecting the simulator with Aspen Plus and selecting the same model. The maximum absolute average deviation between the density and viscosity values obtained in both simulations, for the two case studies analyzed, is lower than 0.7% which demonstrates the proper interconnection. The accuracy of the results obtained modeling multicomponent mixtures and supercritical fluids proves the applicability of the method to any scenarios.
Challenges in Process Optimization for New Feedstocks and Energy Sources Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-19 Alexander Mitsos, Norbert Asprion, Christodoulos A. Floudas, Michael Bortz, Michael Baldea, Dominique Bonvin, Adrian Caspari, Pascal Schäfer
Current and future challenges of optimization in the process industry are discussed. The gap between academic research and industrial workflow is analyzed. Moreover, issues arising from the shift from conventional fossil fuels as both feedstock and energy source to nonconventional feedstocks (shale gas, tar sands, CO2 and biomass) and penetration of intermittent renewable energy are discussed. This manuscript focuses mainly on offline model-based optimization of design and operation, including the generation and selection of promising process alternatives for new feedstocks in conceptual design, multi-objective optimization, the estimation of thermodynamic parameters of new intermediates and the optimization of process operation under the volatile availability of the new feedstocks and energy sources. Moreover, a number of opportunities and needs for research and development are identified, including the simultaneous optimization of feedstocks, processes and products and a production able to process a variety of feedstocks and to utilize energy when it is cheap.
Multiscale Three-Dimensional CFD Modeling for PECVD of Amorphous Silicon Thin Films Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-17 Marquis Crose, Weiqi Zhang, Anh Tran, Panagiotis D. Christofides
The development of a three-dimensional, multiscale computational fluid dynamics (CFD) model is presented here which aims to capture the deposition of amorphous silicon thin films via plasma-enhanced chemical vapor deposition (PECVD). The macroscopic reactor scale and the microscopic thin film growth domains which define the multiscale model are linked using a dynamic boundary which is updated at the completion of each time step. A novel parallel processing scheme built around a message passing interface (MPI) structure, in conjunction with a distributed collection of kinetic Monte Carlo algorithms, is applied in order to allow for transient simulations to be conducted using a mesh with greater than 1.5 million cells. Due to the frequent issue of thickness non-uniformity in thin film production, an improved PECVD reactor design is proposed. The resulting geometry is shown to reduce the product offset from ∼ 25 nm to less than 13 nm using identical deposition parameters.
A Simulation-based Optimization Framework for Integrating Scheduling and Model Predictive Control, and its Application to Air Separation Units Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-14 Lisia S. Dias, Richard C. Pattison, Calvin Tsay, Michael Baldea, Marianthi G. Ierapetritou
The integration of dynamic process models in scheduling calculations has recently received significant attention as a mean to improve operational performance in increasingly dynamic markets. In this work, we propose a novel framework for the integration of scheduling and model predictive control (MPC), which is applicable to industrial size problems involving fast changing market conditions. The framework consists on identifying scheduling-relevant process variables, building low-order dynamic models to capture their evolution, and integrating scheduling and MPC by, i) solving a simulation-optimization problem to define the optimal schedule and, ii) tracking the schedule in closed-loop using the MPC controller. The efficacy of the framework is demonstrated via a case study that considers an air separation unit operating under real-time electricity pricing. The study shows that significant cost reductions can be achieved with reasonable computational times.
A physiologically-based diffusion-compartment model for transdermal administration – The melatonin case study Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-14 Adriana Savoca, Giovanni Mistraletti, Davide Manca
Simultaneous Identification and Optimization of Biochemical Processes under Model-Plant Mismatch Using Output Uncertainty Bounds Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-13 Rubin Hille, Hector M. Budman
The method of simultaneous identification and optimization aims at satisfying the conditions of optimality while providing accurate predictions of the process outputs. The model parameters are updated in a run-to-run procedure as to account for changes in operating points and to correct for errors in the predicted gradients of the cost-function and constraints. To make this parameter updating step more robust, we propose a parameter identification objective that includes a ratio of the sum of squared errors to a parametric gradient sensitivity function. This results in an identified set of parameters which provide larger sensitivities for the subsequent gradient correction step thus leading to faster convergence to the optimum. Moreover, worst-case uncertainty bounds on the model outputs are utilized to enforce an adequate model fitting. This is especially valuable when identifying dynamic metabolic models with many parameters. The resulting improvements are illustrated using two simulated cell culture processes.
Infeasibility resolution for multi-purpose batch process scheduling Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-12 Yash Puranik, Apurva Samudra, Nikolaos V. Sahinidis, Alexander B. Smith, Bijan Sayyar-Rodsari
Scheduling decisions give rise to some of the most challenging optimization problems in the process industry. Formulating mathematical models for scheduling problems and devising tailored solution algorithms for these models has been the main thrust of previous literature. In this work, we focus on analyzing and resolving the cause of bottlenecks and infeasibilities in generic scheduling problems. We present a systematic approach for infeasibility diagnosis. Our approach exploits the known structure of scheduling models to isolate interpretable infeasible sets of constraints. We demonstrate the power of the algorithm on infeasible instances of the Westenberger-Kallrath multipurpose batch process modeled using a state-task network (STN) representation. The methodology presented in the paper is able to successfully analyze the cause of infeasibility and provide recommendations for resolving it. We also demonstrate how these insights and recommendations can be presented to scheduling operators with little or no optimization expertise in an intuitive manner.
CO2 Water-Alternating-Gas Injection for Enhanced Oil Recovery: Optimal Well Controls and Half-Cycle Lengths Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-11 Bailian Chen, Albert C. Reynolds
CO2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO2 injection with the injected water to control the mobility of CO2 and to stabilize the gas front. Optimization of CO2 -WAG injection is widely regarded as a viable technique for controlling the CO2 and oil miscible process. Poor recovery from CO2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However, the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. The proposed framework is demonstrated with three reservoir examples.
Satisfiability Modulo Theories for Process Systems Engineering Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-10 Miten Mistry, Andrea Callia D’Iddio, Michael Huth, Ruth Misener
Process systems engineers have long recognized the importance of both logic and optimization for automated decision-making. But modern challenges in process systems engineering could strongly benefit from methodological contributions in computer science. In particular, we propose satisfiability modulo theories (SMT) for process systems engineering applications. We motivate SMT using a series of test beds and show the applicability of SMT algorithms and implementations on (i) two-dimensional bin packing, (ii) model explainers, and (iii) MINLP solvers.
Heuristics with Performance Guarantees for the Minimum Number of Matches Problem in Heat Recovery Network Design Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-07 Dimitrios Letsios, Georgia Kouyialis, Ruth Misener
Heat exchanger network synthesis exploits excess heat by integrating process hot and cold streams and improves energy efficiency by reducing utility usage. Determining provably good solutions to the minimum number of matches is a bottleneck of designing a heat recovery network using the sequential method. This subproblem is an NP NP -hard mixed-integer linear program exhibiting combinatorial explosion in the possible hot and cold stream configurations. We explore this challenging optimization problem from a graph theoretic perspective and correlate it with other special optimization problems such as cost flow network and packing problems. In the case of a single temperature interval, we develop a new optimization formulation without problematic big-M parameters. We develop heuristic methods with performance guarantees using three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy packing. Numerical results from a collection of 51 instances substantiate the strength of the methods.
Enhancing Natural Gas-to-Liquids (GTL) Processes Through Chemical Looping for Syngas Production: Process Synthesis and Global Optimization Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-07 William W. Tso, Alexander M. Niziolek, Onur Onel, C. Doga Demirhan, Christodoulos A. Floudas, Efstratios N. Pistikopoulos
A process synthesis and global optimization framework is presented to determine the most profitable routes of producing liquid fuels from natural gas through competing technologies. Chemical looping is introduced into the framework for the first time as a natural gas conversion alternative. The underlying phenomena in chemical looping are complex and models from methods such as computational fluid dynamics are unsuitable for global optimization. Therefore, appropriate approximate models are required. Parameter estimation and disjunctive programming are described here for modeling two chemical looping processes. The first is a nickel oxide based process developed at CSIC in Spain; the second is a iron oxide based process developed at Ohio State University. These mathematical models are then incorporated into a comprehensive process superstructure to evaluate the performance of chemical looping against technologies such as autothermal reforming and steam reforming for syngas production. The rest of the superstructure consists of process alternatives for liquid fuels production from syngas and simultaneous heat, power, and water integration. Among the various case studies considered, it is shown that chemical looping can reduce the break-even oil prices for natural gas-to-liquids processes by as much as 40%, while satisfying production demands and obeying environmental constraints. For a natural gas price of $5/TSCF, the break-even price is as low as $32.10/bbl. Sensitivity analysis shows that these prices for chemical looping remain competitive even as natural gas cost rises. The findings suggest that chemical looping is a very promising option to enhance natural gas-to-liquids processes and their capabilities.
Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-03-03 Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu
Iterative optimization technology (IOT), a method that predicts the component composition from only the infrared (IR) spectra of the pure components and mixtures by using Beer's law, has been proposed to reduce the number of calibration samples for process analytical technology in the pharmaceutical industry. However, IOT cannot be applied to mixtures that have wavelength regions where Beer's law does not hold, such as liquid mixtures. The objective of this study is to apply IOT to liquid mixtures to realize a calibration-minimum method. We propose a novel calibration-minimum method that formulates spectral changes by polynomials of the mole fractions considering reasonable boundary conditions for online monitoring. The prediction ability of the proposed method was verified by three case studies: two binary mixtures and one ternary mixture. The model selection strategy, conditions for calibration, and estimation of missing pure component spectra are also discussed. This research represents a step towards advanced calibration-minimum methods.
Identification of Systems with Slowly Sampled Outputs using LPV model ☆ Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-27 Wengang Yan, Yucai Zhu, Lingyu Zhu, Xin Liu
Identification of systems with slowly sampled output is studied. A linear parameter varying (LPV) model with multi-model structure is used to solve the problem. The output error (OE) method is used to estimate model parameters. Firstly, the local models and weighting functions are estimated separately using optimization methods. Then, a relaxation iteration method is developed to refine the parameters of the total model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The method is verified using both simulation data and industrial data. In the industrial case study, the LPV models give more accurate prediction of product qualities than that of a linear dynamic model and that of a static nonlinear model; the result also indicates the necessity of using test signals in soft-sensor development.
An integrated output space partition and optimal control method of multiple-model for nonlinear systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-27 Chunyue Song, Bing Wu, Jun Zhao, Zuhua Xu
A systematic method for optimally partitioning operating range of each linear subsystem in output space under the criterion of closed-loop performance is initiated, when a multiple model approach is applied to nonlinear systems. As a result, an integrated output space partition and optimal control method is proposed. Firstly, linear input-output models are identified at given operating points and then reformulated as a hybrid model underlying each state having the same physical meaning. Secondly, the optimal state space partition is obtained according to a closed-loop control index. Finally, based on the obtained optimal state space partition, an optimal output space partition is achieved with the projection technique. Furthermore, a hybrid model-MPC strategy is designed according to the obtained multiple-model associated with its optimal output space partition. The integrated output space partition and optimal control method can improve the nonlinear system overall control performance and results of numerical simulation are provided.
Modelling the Effect of Temperature on the Gel-Filtration Chromatographic Protein Separation Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-24 Gorgi Pavlov, James T. Hsu
In this study a mechanistic model for chromatography was taken and the solution to its Laplace transfer function was obtained using the Fast Fourier Transform method. Using previously developed correlations for modelling diffusion, both in solution and intraparticle, and estimating the mass transfer coefficient, the effect of temperature in gel fittration liquid chromatography was investigated. The effect of each individual parameter on the elution curve was systematically explored allowing for reasonable estimates for the different temperature cases. In this case, a system of bovine serum albumin and phenylalanine separated by gel filtration chromatography was simulated to demonstrate how the resolution and the selectivity of the separation will change with physical parameters. Decreasing the particle size and flow rate while increasing the temperature led to higher resolution, which is consistent with experimental literature data.
Multiobjective optimization and experimental validation for batch cooling crystallization of citric acid anhydrate Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-22 K. Hemalatha, P. Nagveni, P. Naveen, K. Yamuna Rani
Multiobjective optimization (MOO) of crystallization systems is gaining importance due to its ability to handle multiple conflicting objectives together for finding optimal operating policies. The present study focuses on batch cooling crystallization of citric acid. Among the two forms of citric acid, citric acid anhydride (CAA) is chosen for experimentation as no such study is available. MOO is carried out to seek optimal cooling policy for unseeded cooling crystallization of CAA to maximize mean crystal size while minimizing variance in size. In this procedure, temperature is discretized using piecewise constant-control vector parameterization which is simple and convenient for practical implementation. The model reported in literature is suitably modified for solubility parameters which are verified experimentally, and employed for optimization. One of the optimal solutions from the Pareto solution set is implemented through experimentation successfully and the measured product crystal properties are comparable to the predicted results obtained through optimization.
An Anchor-Tenant Approach to the Synthesis of Carbon-Hydrogen-Oxygen Symbiosis Networks ☆ Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-22 Kevin Topolski, Mohamed M.B. Noureldin, Fadwa T. Eljack, Mahmoud M. El-Halwagi
Sustainable development of industrial cities and eco-industrial parks (EIPs) requires careful consideration and creation of synergistic opportunities among the participating entities. Recently, a multi-scale design approach was developed for carbon-hydrogen-oxygen symbiosis networks (CHOSYNs) with focus on the targeting, integration, and retrofitting of EIPs involving a set of existing facilities. Another important category of EIPs involves the grass-root design of industrial cities in which the participants are not originally known. Instead, “anchor” plants are first invited followed by the consideration and invitation of supporting facilities (referred to as “tenants”) that are to be determined according to integration opportunities with the anchors, other tenants, common infrastructure while accounting for resource limitations, market demands, and environmental regulations. The purpose of this work is to introduce a multi-scale targeting, synthesis, and optimization approach for the grass-root design of EIPs with known anchors. The CHOSYN framework is extended to tackle the case of candidate tenants with the objective of identifying industrial facilities, raw materials, byproducts, products, and wastes that can be effectively integrated with the anchors, among the participating tenants, and with the surrounding markets. Atomic-based and techno-economic targeting approaches are developed to identify benchmarks for mass integration within the EIP and to provide preliminary screening of the type and size of candidate tenants. Next, an optimization framework is developed to synthesize a highly-integrated and cost-effective cluster of anchors and tenants with sufficient design details on the individual facilities and the interaction among the participating plants. A case study is solved to demonstrate the multi-scale targeting, synthesis, and optimization approaches for the grass-root design of EIPs.
Simultaneous optimization and heat integration of the coal-to-SNG process with a branched heat recovery steam cycle Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-22 Bo Huang, Yang Li, Rui Gao, Yongfei Zuo, Zhenghua Dai, Fuchen Wang
The coal-to-SNG process is an energy-intensive process, and optimizing the heat recovery network can improve the economy and energy efficiency. This study proposes a branched, triple pressure level heat recovery steam cycle (HRSC) to recover waste heat, in which one branch is responsible for recovering the waste heat from the water gas shift (WGS) unit, and the other branch is responsible for the methanation (METH) unit. The extended Duran-Grossmann model is used to optimize two heat exchanger networks to match the branched HRSC superstructure. The temperature/pressure/flow rates of the HRSC streams and the operating temperature of the WGS and METH units are optimized. The optimal bypass ratio of the WGS unit as well as the recycle ratio and split ratio of the METH unit, are 0.506, 0.681 and 0.456, respectively. The exergy efficiency of the coal-to-SNG plant is improved by 1.28% compared with the industrial plant, which can reach 54.17%.
A stoichiometric method for reducing simulation cost of chemical kinetic models Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-22 Emmanuel A. Amikiya, Mapundi K. Banda
Mathematical models for chemically reacting systems have high degrees of freedom (very large) and are computationally expensive to analyse. In this discussion, we present and analyse a model reduction method that is based on stoichiometry and mass balances. This method can significantly reduce the high degrees of freedom of such systems. Numerical simulations are undertaken to validate and establish efficiency of the method. A practical example of acid mine drainage is used as a test case to demonstrate the efficacy of the procedure. Analytical results show that the stoichiometrically-reduced model is consistent with the original large model, and numerical simulations demonstrate that the method can accelerate convergence of the numerical schemes in some cases.
Elucidating Multi-Targeted Anti-Amyloid Activity and Enhanced Islet Amyloid Polypeptide Binding of β-wrapins Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-21 Asuka A. Orr, Hamed Shaykhalishahi, Ewa A. Mirecka, Sai Vamshi R. Jonnalagadda, Wolfgang Hoyer, Phanourios Tamamis
β-wrapins are engineered binding proteins stabilizing the β-hairpin conformations of amyloidogenic proteins islet amyloid polypeptide (IAPP), amyloid-β, and α-synuclein, thus inhibiting their amyloid propensity. Here, we use computational and experimental methods to investigate molecular recognition of IAPP by β-wrapins. We show that the multi-targeted, IAPP, amyloid-β, and α-synuclein, binding properties of β-wrapins originates mainly from optimized interactions between β-wrapin residues and sets of residues in the three amyloidogenic proteins with similar physicochemical properties. Our results suggest that IAPP is a comparatively promiscuous β-wrapin target, probably due to the low number of charged residues in the IAPP β-hairpin motif. The sub-micromolar affinity of β-wrapin HI18, specifically selected against IAPP, is achieved in part by salt-bridge formation between HI18 residue Glu10 and the IAPP N-terminal residue Lys1, both located in the flexible N-termini of the interacting proteins. Our findings provide insights towards developing novel protein-based single or multi-targeted therapeutics.
Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-21 Burcu Beykal, Fani Boukouvala, Christodoulos A. Floudas, Efstratios N. Pistikopoulos
The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.
On the Estimation of High-Dimensional Surrogate Models of Steady-State of Plant-wide Processes Characteristics Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-21 Anh Phong Tran, Christos Georgakis
This work generalizes a preliminary investigation (Georgakis and Li, Ind. Eng. Chem. Res. 2010, 49 (17), 8035-8047) in which we examined the use of Response Surface Methodology (RSM) for the estimation of surrogate models as accurate approximations of high-dimensional knowledge-driven models. Three processes are examined with higher complexity than before, accounting for a much larger number of input and output variables. The surrogate models obtained are used to analyze several steady-state plant-wide characteristics. In all processes, the knowledge-driven model is a dynamic simulation with a plant-wide control structure of multiple SISO controllers. This type of controller proves to not be robust enough in its stability characteristics to enable substantial changes in the set-points. The net-elastic regularization is successfully used for the estimation of the metamodel parameters, avoiding overfitting and eliminating insignificant terms. Cross validation is used to compare and evaluate the relative accuracy of the quadratic and cubic models.
Analysis of the dynamics of an active control of the surface potential in metal oxide gas sensors Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-21 Oscar Monge-Villora, Manuel Dominguez-Pumar, Josep M. Olm
Gas sensing is nowadays a key actor in pollution observation and detection of chemical toxic agents or explosives. All these applications require the shortest possible time response. Very recently, a control of the surface potential in gas sensors based on metal oxides has experimentally shown to dramatically improve the time response of metal-oxide gas sensors. The proposed control is inspired in sigma-delta modulators. This paper aims at studying the resulting dynamics in the sensor from a theoretical point of view. Using state space models, it is shown how the state variables, namely the concentrations of ionized species in the sensing layer, evolve with time in open and closed loop configuration. This analysis studies how it is possible to alter the dynamics of the overall system, while at the same time keeping some important characteristics of sigma-delta modulators, such as quantization noise-shaping. Numerical simulations validate the obtained results.
A POMDP Framework for Integrated Scheduling of Infrastructure Maintenance and Inspection Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-19 Jong Woo Kim, Go Bong Choi, Jong Min Lee
This work presents an optimization scheme for maintenance and inspection scheduling of the infrastructure system whose states are nearly impossible or prohibitively expensive to estimate or measure online. The suggested framework describes state transition under the observation uncertainty as Partially Observable Markov Decision Process (POMDP) and can integrate heterogeneous scheduling jobs including maintenance, inspection, and sensor installation within a single model. The proposed approach performs survival analysis to obtain time-variant transition probabilities. A POMDP problem is then formulated via state augmentation. The resulting large-scale POMDP is solved by an approximate point-based solver. We exploit the idea of receding horizon control to the POMDP framework as a feedback rule for the online evaluation. Water distribution pipeline is analyzed as an illustrative example, and the results indicate that the proposed POMDP framework can improve the overall cost for maintenance tasks and thus the system’s sustainability.
Combining multi-attribute decision-making methods with multi-objective optimization in the design of biomass supply chains Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-17 J. Wheeler, M.A. Páez, G. Guillén-Gosálbez, F.D. Mele
Multi-objective optimization (MOO) is widely applied in sustainability problems where several objectives are accounted for. Unfortunately, the complexity grows with the number of objectives considered. In this paper, we simplify MOO problems via their combination with multi-attribute decision-making (MADM) methods. The approach identifies a unique Pareto solution of the MOO problem, which best reflects the decision-makers’ preferences, by using weighting factors generated via four well-known MADM methods: SWING, SMART, AHP and TRADE OFF. The capabilities of this approach are illustrated through its application to the design and planning of a sugar/ethanol supply chain using questionnaires filled in by academic experts in the problem. The weights obtained may differ significantly from the ones given by standard life-cycle methods used in systems engineering problems. Overall, our approach simplifies the MOO problem by identifying solutions consistent with the decision-makers’ preferences and by providing valuable insight on how these preferences are articulated in practice.
Integrating decisions of product and closed-loop supply chain design under uncertain return flows Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-16 Luis J. Zeballos, Carlos A. Méndez, Ana P. Barbosa-Povoa
The shortage of natural resources, the need to take into account societal considerations, the emergence of new government regulations and the necessity to maintain and/or improve the economic benefit of the supply chain, have created a growing awareness on academia as well as industries towards the development of closed-loop supply chains (CLSCs), where explicitly products' life-cycles are accounted for. Concentrating on the problems of the product and network design for a multi-product, multi-echelon and multi-period CLSC, in this work a two-stage stochastic mixed integer linear model incorporating uncertainty on the quality and quantity of the return flows is proposed. In addition, risk management related to critical uncertain parameters is performed, where a conditional value at risk (CVaR) concept is applied to supply chain profits. The formulation considers decisions associated with the network design and, simultaneously, with the products to manufacture (new and remanufactured) and their associated raw materials (new and recovered). A network superstructure is considered accounting for two types of customers (first and second markets), raw material suppliers, factories, distribution centers, customer demands, recovery centers, recycle centers, final disposal locations and re-distribution centers. Optimal solutions with high economic and environmental benefits are obtained where the advantages of using the proposed approach are shown. A case study from a European consumer goods company is explored.
Data-Driven Decision Making under Uncertainty Integrating Robust Optimization with Principal Component Analysis and Kernel Smoothing Methods Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-15 Chao Ning, Fengqi You
This paper proposes a novel data-driven robust optimization framework that leverages the power of machine learning and big data analytics for decision-making under uncertainty. By applying principal component analysis to uncertainty data, correlations between uncertain parameters are effectively captured, and latent uncertainty sources are identified. These data are then projected onto each principal component to facilitate extracting distributional information of latent uncertainties using kernel density estimation techniques. To explicitly account for asymmetric distributions, we introduce forward and backward deviation vectors into the data-driven uncertainty set, which are further incorporated into novel data-driven static and adaptive robust optimization models. The proposed framework not only significantly ameliorates the conservatism of robust optimization, but also enjoys computational efficiency and wide-ranging applicability. Three applications on optimization under uncertainty, including model predictive control, batch production scheduling, and process network planning, are presented to demonstrate the applicability of the proposed framework.
Global Optimization of Grey-Box Computational Systems Using Surrogate Functions and Application to Highly Constrained Oil-Field Operations Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-15 Burcu Beykal, Fani Boukouvala, Christodoulos A. Floudas, Nadav Sorek, Hardikkumar Zalavadia, Eduardo Gildin
This work presents recent advances within the AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for optimization of systems which lack analytical forms and derivatives. A new parallel version of ARGONAUT (p-ARGONAUT) is introduced to solve high dimensional problems with a large number of constraints. This development is motivated by a challenging case study, namely the operation of an oilfield using water-flooding. The objective of this case study is the maximization of the Net Present Value over a five-year time horizon by manipulating the well pressures, while satisfying a set of complicating constraints related to water-cut limitations and water handling and storage. Dimensionality reduction is performed via the parametrization of the pressure control domain, which is then followed by global optimization of the constrained grey-box system. Results are presented for multiple case studies and the performance of p-ARGONAUT is compared to existing derivative-free optimization methods.
Optimal Cryogenic Processes for Nitrogen Rejection from Natural Gas Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-13 Homa Hamedi, Iftekhar A Karimi, Truls Gundersen
Nitrogen rejection processes are usually needed for two natural gas sources: sub-quality natural gas reserves and produced gas from enhanced oil/gas recovery technologies. The nitrogen content of natural gas in the former is usually constant during the project lifetime, but it varies from 5 to 70% during enhanced oil/gas recovery programs. This variation leads to different process flowsheets for nitrogen removal: single-column, double-column, three-column, and two-column processes. In order to determine which configuration is more suitable for a particular nitrogen content in a feed stream, we must minimize the energy requirement for each process. In this study, we merge all the four configurations into two categories: single-column and multi-column processes and then use the Particle Swarm Optimization algorithm to optimize process parameters for each process with the objective of energy consumption minimization. Finally, we use the exergy concept to analyze theoretically these different processes.
Modeling Sex Differences in the Renin Angiotensin System and the Efficacy of Antihypertensive Therapies Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-13 Jessica Leete, Susan Gurley, Anita Layton
The renin angiotensin system is a major regulator of blood pressure and a target for many anti-hypertensive therapies; yet the efficacy of these treatments varies between the sexes. We use published data for systemic RAS hormones to build separate models for four groups of rats: male normotensive, male hypertensive, female normotensive, and female hypertensive rats. We found that plasma renin activity, angiotensinogen production rate, angiotensin converting enzyme activity, and neutral endopeptidase activity differ significantly among the four groups of rats. Model results indicate that angiotensin converting enzyme inhibitors and angiotensin receptor blockers induce similar percentage decreases in angiotensin I and II between groups, but substantially different absolute decreases. We further propose that a major difference between the male and female RAS may be the strength of the feedback mechanism, by which receptor bound angiotensin II impacts the production of renin.
Simulation of a triple effect evaporator of a solution of caustic soda, sodium chloride, and sodium sulfate using Aspen Plus Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-13 Raul Alejandro Vazquez Rojas, Francisco Javier Garfias Vásquez, Enrique Rodolfo Bazua Rueda
Worldwide, the Chlor-Alkali process is the most well-known method for the production of chlorine (Cl2) and sodium hydroxide (NaOH). NaOH, also known as caustic soda, is a very important alkali with many applications in the processing and production of paper, detergents, aluminum, petrochemicals, inorganics, and in the food industry. The aqueous solution of caustic soda, known as “cell liquor," produced in this process must be concentrated from 11 to 50 percent weight, which is achieved through a multiple effect evaporator system. In some cases, the brine used as a raw material carries a few other components that cannot be separated before the feeding of the brine into the process. The presence of sulfates and chloride ions in addition to high NaOH concentrations (0.44 – 0.5 mass fraction) and high temperatures (above 60°C, at 86 mmHg) causes the precipitation of a triple salt (Na2SO4 · NaCl · NaOH). This work focuses on using and validating the model of a triple effect evaporator in Aspen Plus using plant data. According to our results, lower temperatures and the extraction of sulfates could reduce the proportion of triple salt that precipitates in the last stages of the evaporator.
Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition: Application to hydraulic fracturing Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-02-09 Harwinder Singh Sidhu, Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sang-Il Kwon
Developing reduced-order models for nonlinear parabolic partial differential equation (PDE) systems with time-varying spatial domains remains a key challenge as the dominant spatial patterns of the system change with time. To address this issue, there have been several studies where the time-varying spatial domain is transformed to the time-invariant spatial domain by using an analytical expression that describes how the spatial domain changes with time. However, this information is not available in many real-world applications, and therefore, the approach is not generally applicable. To overcome this challenge, we introduce sparse proper orthogonal decomposition (SPOD)-Galerkin methodology that exploits the key features of ridge and lasso regularization techniques for the model order reduction of such systems. This methodology is successfully applied to a hydraulic fracturing process, and a series of simulation results indicates that it is more accurate in approximating the original nonlinear system than the standard POD-Galerkin methodology.
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