Design and Control of Diphenyl Carbonate Reactive Distillation Process with Thermally Coupled and Heat-Integrated Stages Configuration Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-15 Hao-Yeh Lee, Chien-Ying Chen, Jun-Lin Chen, J. Rafael Alcántara-Avila, Masataka Terasaki, Ken-Ichiro Sotowa, Toshihide Horikawa
It has been extensively proven that thermally coupled distillation columns can effectively use less energy than their conventional counterparts. Similarly, the extension to reactive systems has also shown that thermally coupled reactive distillation columns can reduce the energy consumption in comparison with their conventional reactive distillation counterparts. This work aims to show that by realizing heat integration between thermally coupled columns at different pressure, further energy savings can be attained. The diphenyl carbonate production process has been taken up to show that there is a synergistic effect when thermally coupling and heat integration are combined in the same distillation sequence. The results showed that the proposed system could attain 47% energy savings in comparison with conventional a reactive distillation sequence while keeping good rejection of throughput and feed composition disturbances.
A New Termination Criterion for Sampling for Surrogate Model Generation using Partial Least Squares Regression Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-15 Julian Straus, Sigurd Skogestad
This paper proposes a new incremental sampling method for the generation of surrogate models based on the application of partial least squares regression (PLSR) as a termination criterion. Compared to existing incremental and adaptive methods, the proposed method allows the sampling algorithm to stop without needing to fit a surrogate model at each iteration step. The proposed procedure was applied to a motivating pipe model and two case studies; the reaction and the separation section of an ammonia synthesis loop. In all cases, the new sampling method allows a small number of sampling points, corresponding to a regular grid with less than two points in each independent variable. The two surrogate models of the ammonia loop are combined for overall optimization. The optimum for the combined surrogate models is close to the optimum obtained with the original model.
Modeling of Reactive Batch Distillation Processes for Control Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-16 Alejandro Marquez-Ruiz, Carlos S. Méndez-Blanco, Leyla Özkan
Reactive batch distillation (RBD) is a preferred process intensification technology to carry out equilibrium-limited reactions. It is a multicomponent, multiphase system. Appropriate process description requires dynamic modelling of coupled thermodynamics and transport phenomena including the chemical reactions. Such models are barely applicable for online model based operation technology such as model predictive control, real time optimization and online process monitoring. Therefore, in this paper, the rigorous dynamic model of an RBD is transformed into a set of decoupled ordinary differential equations using linear transformation matrices, called extent transformation, that preserve the physical meaning of the transformed variables. The resulting model has a state space representation with a diagonal state matrix. This representation is suitable for control purposes and can be considered as a linear parameter varying system. Based on the final structure of the model, controllability conditions are stated, and model reduction scenarios are proposed. Finally, the model based on extent transformations is compared with the rigorous nonlinear model via the simulation of a polyesterification process.
Deterministic Global Process Optimization: Accurate (Single-Species) Properties via Artificial Neural Networks Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-16 Artur M. Schweidtmann, Wolfgang R. Huster, Jannik T. Lüthje, Alexander Mitsos
Global deterministic process optimization problems have recently been solved efficiently in a reduced-space by automatic propagation of McCormick relaxations [Bongartz & Mitsos 2017]. However, the previous optimizations have been limited to simplified thermodynamic property models. Herein, we propose a method that learns accurate thermodynamic properties via artificial neural networks (ANNs) and integrates those in deterministic global process optimization. The resulting hybrid process model is solved using the recently developed method for deterministic global optimization problems with ANNs embedded [Schweidtmann & Mitsos 2017]. The optimal operation of a validated steady state model of an organic Rankine cycle is solved as a case study. It is especially challenging as the thermodynamic properties are given by the implicit Helmholtz equation of state. The results show that modeling of thermodynamic properties via ANNs performs favorable in deterministic optimization. This method can rapidly be extended to include properties from existing thermodynamic libraries, based on models or data.
A multi-objective optimization approach for sustainable water management for places with over-exploited water resources Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-12 Salvador I. Pérez-Uresti, José María Ponce-Ortega, Arturo Jiménez-Gutiérrez
Rainwater harvesting (RWH) is analyzed in this work as an option for water supply for places with over-exploited water resources. The model is formulated under a multi-period, multi-objective optimization model. The objective is two fold, first to assess the potential of RWH as an alternative water source, and second to design an optimal water distribution network in which both natural and alternative sources work as an integrated system. The problem is formulated to account for three different objectives, namely maximum profit, minimum groundwater usage and minimum investment cost. A case study for the city of Queretaro in Mexico was considered to show the applicability of the proposed approach. The results show that as much as 27% of the domestic demand in Queretaro City could be supplied by RWH, which would lead to a significant recovery of deep wells currently under depletion.
A model-based optimization of microalgal cultivation strategies for lipid production under photoautotrophic condition Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-10 Kyung Hwan Ryu, Boeun Kim, Jay H. Lee
Increasing the lipid production rate stands as one of the challenges for achieving economic feasibility of microalgal biorefinery. Various bioreactor operation strategies have been considered for culturing microalgae, including batch, continuous, fed-batch, semi-batch, and two-stage operations. However, since the previous studies used different experimental criteria, it is hard to draw a conclusion about their relative performances. Motivated by this, the present study compares lipid productivity performances and capital expenditures of the various operation strategies after their operating conditions are optimized. The optimal condition for each strategy is determined by performing a model-based optimization to maximize lipid productivity, which is a good indicator of the overall economics. The two-stage operation with continuous-batch serial connection (w/ stress condition) and the semi-batch operation (w/o stress condition) show outstanding performance compared to the other types of operation. Also, start-up, initial cell concentration, and chemical consumption, which are critical factors in large-scale cultivation, are analyzed.
Wide Spectrum Feature Selection (WiSe) for Regression Model Building Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-10 Ricardo Rendall, Ivan Castillo, Alix Schmidt, Swee-Teng Chin, Leo H. Chiang, Marco Reis
Developing predictive models from industrial datasets implies the consideration of many possible predictor variables (features). Using all available features for data-driven modelling is not recommended, as most of them are expected to be irrelevant and their inclusion in the model may compromise robustness and accuracy. In this work, we present, test and compare a new two-stage feature selection method called wide spectrum feature selection for regression (WiSe). In the first stage, a combination of efficient bivariate filters analyzes linear and non-linear association patterns between predictors and responses, screening out clearly noisy features. In the second stage, the reduced set of retained features is subject to further selection in the scope of the predictive methods considered, optimizing their predictive performance. Three simulated datasets and an industrial case illustrate the effectiveness and benefits of applying WiSe to support model development in a wide range of high-dimensional regression problems.
Performance evaluation of multi-stage reverse osmosis process with permeate and retentate recycling strategy for the removal of chlorophenol from wastewater Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-11 M.A. Al-Obaidi, C. Kara-Zaïtri, I.M. Mujtaba
Reverse Osmosis (RO) is one of the most widely used technologies for wastewater treatment for the removal of toxic impurities, such as phenol and phenolic compounds from industrial effluents. In this research, performance of multi-stage RO wastewater treatment system is evaluated for the removal of chlorophenol from wastewater using model-based techniques. A number of alternative configurations with recycling of permeate, retentate, and permeate-retentate streams are considered. The performance is measured in terms of total recovery rate, permeate product concentration, overall chlorophenol rejection and energy consumption and the effect of a number of operating parameters on the overall performance of the alternative configurations are evaluated. The results clearly show that the permeate recycling scheme at fixed plant feed flow rate can remarkably improve the final chlorophenol concentration of the product despite a reduction in the total recovery rate.
Logarithmic mean: Chen's approximation or explicit solution? Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-09 J.J.J. Chen
An explicit solution has been obtained for the logarithmic mean temperature difference method of heat exchanger calculation by making use of the Lambert W-function. The results might be of use where an explicit solution involving the logarithmic mean is required.
From Ontology to Executable Program Code Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-06 Arne Tobias Elve, Heinz A Preisig
The implementation of coded mathematical process models is regarded as a cumbersome and challenging task, reasons being that the modeller needs to have expertise both in modelling and computer science. Our ProcessModellerSuite implements a staged approach to modelling starting with the formulation of a context-dependent ontology defining a structure against which the mathematical representation of the principal model components is defined. Process models are then generated by interactively constructing a graph of communicating principle components, which enables the generation of arbitrary complex process models and intermediate storage of customised unit models. This storage of unit models forms the equivalent of the traditional unit-operations libraries, by allowing for insertion of the unit models into other graphs. A task builder combines the information from the graph with the used model components to automatically generate executable program code of the process model, which will be the topic of this paper.
Performance enhancement of pressure-swing distillation process by the combined use of vapor recompression and thermal integration Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-05 Qingjun Zhang, Meiling Liu, Aiwu Zeng
Model-Based Bidding Strategies on the Primary Balancing Market for Energy-Intense Processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-05 Pascal Schäfer, Hermann Graf Westerholt, Artur M. Schweidtmann, Svetlina Ilieva, Alexander Mitsos
Design of plantwide control and safety analysis for diethyl oxalate production via regeneration-coupling circulation by dynamic simulation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-04 zhu Jiaxing, Hao Lin, Bai Wenshuai, zhang Bo, Pan Bochen, Wei Hongyuan
In this article, the plantwide control of a novel process for diethyl oxalate production via two steps is investigated. The unique feature of this process is that there is a closed regeneration-coupling circulation. It results in that two steps should be matched properly and mass balance for overall reaction should be satisfied precisely. An effective control structure using a feedforward ratio with composition controller is determined.Later, safety analysis for this process is investigated by the integration of dynamic simulation and HAZOP (Hazard and Operability Analysis). In comparison with heuristic HAZOP, quantitative deviations can be introduced. Quantitative variation trends and change rates of important variables can be determined. Determining increase rate in temperature and pressure is significantly important, since response time as indirect indictor can be used to assess the possibility of risk. Finally, a general procedure based on simulation for design and safety analysis of chemical process is proposed.
Agent-oriented simulation framework for handling disruptions in chemical supply chains Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-02 Behzad Behdani, Zofia Lukszo, Rajagopalan Srinivasan
To cope with increasing vulnerability, global business especially chemical manufacturing companies need to actively manage (the risk of) disruptive events in their supply chains. This calls for systematic frameworks to guide their efforts. Further, due to the complexity of today's global supply chains, decision making tools are needed to provide support in different stages of the supply chain disruption management process. This paper presents an agent-oriented simulation framework for disruption management in supply chains. This simulation framework provides a flexible modelling and simulation environment for decision makers to experiment with different types of disruptions and disruption management strategies. The application of the simulation model to support decision-making in different steps of the pre- and post-disruption management processes is illustrated using a lube oil supply chain case study.
Optimal ()-reliable design of distributed energy supply systems Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-10-02 Dinah Elena Hollermann, Dörthe Franzisca Hoffrogge, Fabian Mayer, Maike Hennen, André Bardow
Chromatographic studies of n-Propyl Propionate: adsorption equilibrium, modelling and uncertainties determination Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-27 Idelfonso B.R. Nogueira, Rui P.V. Faria, Reiner Requião, Hannu Koivisto, Márcio A.F. Martins, Alírio E. Rodrigues, José M. Loureiro, Ana M. Ribeiro
The n-Propyl Propionate (ProPro) is a compound that has several possible industrial applications. However, the current production route of this component presents several problems, such as the downstream purification. In this way, chromatographic separation could be an alternative solution to the downstream purification. In this work experimental studies of the ProPro reaction system separation in a chromatographic fixed bed unit packed with Amberlyst 46 were performed. The adsorption equilibrium isotherms and the corresponding Langmuir model parameters were determined. A phenomenological model to represent the process was developed and validated through the experimental data. Meanwhile, it is proposed the characterization of the uncertainties of all steps and its extension to the model prediction, which allowed to estimate the model parameters with a reduced number of experiments, when compared with other reports in the literature; nevertheless, the final results lead to a statistically more reliable model.
Side Stream Control in Semicontinuous Distillation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-25 Pranav Bhaswanth Madabhushi, Thomas A. Adams
The idea to reduce cycle time (T), by controlling the side stream flow rate using a feedforward control model – the ideal side draw recovery arrangement (ISR) – was standard in most semicontinuous distillation studies. However, its effect, particularly on ‘T’ and more broadly on the system dynamics, was not clearly understood. In the current study, we compare the performance of using a modified form of ISR model with the status quo, based on the criteria, T and separating cost (SC) on different case studies. Results show that the modified control model performed better with a 10-20% reduction in SC while maintaining product purities. Furthermore, the side stream flow rate trajectory that minimizes SC was found by using dynamic optimization and it did not differ a lot from the trajectory generated by the modified control model. The improvement in SC was at most 2%.
Optimal Municipal Solid Waste Energy Recovery and Management: A Mathematical Programming Approach Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-26 Jaime Garibay-Rodriguez, Maria G. Laguna-Martinez, Vicente Rico-Ramirez, Jose E. Botello-Alvarez
A multi-period approach to municipal solid waste (MSW) management is proposed. The analysis includes the optimization of a MSW network considering waste reduction processes and landfilling. The optimization of the transportation of MSW to its potential destinations has been addressed using a direct-hauling system and an optimally allocated off-municipality transfer station. As the main component in the formulation, an optimal landfill gas (LFG) to energy design is obtained to improve the economics of the landfill operation; the design involves the installation of several harnessing technologies according to the annual increase or decay of the LFG flow rate. A case-study for a municipality in Mexico has been solved through the GAMS® modeling environment. The resulting Mixed-Integer Linear Programming (MILP) model has been assessed through several scenarios. The results show that the installation of an LFG-to-electricity system and a materials recycling facility achieve the minimum overall cost of the MSW management.
An improvement scheme for pressure-swing distillation with and without heat integration through an intermediate connection to achieve energy savings Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-25 Yinglong Wang, Kang Ma, Mengxiao Yu, Yao Dai, Rujia Yuan, Zhaoyou Zhu, Jun Gao
Classification of states and model order reduction of large scale Chemical Vapor Deposition processes with solution multiplicity Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-25 E.D. Koronaki, P.A. Gkinis, L. Beex, S.P.A. Bordas, C. Theodoropoulos, A.G. Boudouvis
This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by detailed, high-fidelity models, but can also use spatio-temporal measurements. The Reduced Order Model (ROM) is built using the method-of-snapshots variant of the Proper Orthogonal Decomposition (POD) method and Artificial Neural Networks (ANN) for the identification of the time coefficients. The derivation of the model is completely equation-free as it circumvents the projection of the actual equations onto the POD basis. Prior to building the model, the Support Vector Machine (SVM) supervised classification algorithm is used in order to identify clusters of data corresponding to (physically) different states that may develop at the same operating conditions due to the inherent nonlinearity of the process. The different clusters are then used for ANN training and subsequent development of the ROM. The results indicate that the ROM is successful at predicting the dynamic behavior of the system in windows of operating parameters where steady states are not unique.
Determining the number of segments for piece-wise linear representation of discrete-time signals Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-22 Jiandong Wang, Yan Yu, Kuang Chen
Piece-wise linear representation (PLR) separates a discrete-time continuous-valued signal into a few short data segments, each of which is represented by a straight line. One prerequisite parameter in the PLR is the number of data segments. This paper proposes a new method to determine the number of data segments. The method is based on a key observation on two balancing effects of the number of data segments on the confidence intervals of line approximations. The confidence intervals define parallelogram-shaped spaces, and an index is formulated as the weighted percentage of these spaces occupied by data points in data segments. The number of data segments is determined as the one achieving the maximum value of the formulated index. Numerical and industrial examples are provided to illustrate the effectiveness of the proposed method.
Dynamic Optimization of Batch Free Radical Polymerization with Conditional Modeling Formulation through the Adaptive Smoothing Strategy Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-23 Jing Kong, Xi Chen
With the increase of conversion and viscosity, a batch free radical polymerization (FRP) may encounter complicated phenomena, including the gel, glass, and cage effects. These effects normally lead to a conditional modeling formulation described by a set of differential-algebraic equations (DAEs). Discontinuous gradient exists at model switches triggered by occurrence of the effects, which cause difficulty for dynamic optimization on the batch process. To deal with the discontinuity, interior-point smooth approximations are proposed to describe the model switches at the occurrence of the effects. An adaptive smoothing strategy, which can efficiently balance the efficiency of optimization and the accuracy of the approximated model, is proposed for the dynamic optimization. To show the effectiveness of this smoothing strategy, case studies for optimal operating profiles in a polymerization process of methyl methacrylate are conducted. Good computational performance is achieved with the proposed strategy.
Numerical investigation of selective withdrawal in a pancreatic cell islet encapsulation apparatus Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-24 Nikos G. Dimitrioglou, Dimitris T. Hatziavramidis
The development of an efficient microencapsulation apparatus is a major challenge for islet transplantation. To that end, the flow generated by selective withdrawal in an apparatus for micro-encapsulation of pancreatic islets is studied through CFD simulations. Each islet enters individually a chamber containing a twolayer system in which is encapsulated by selective withdrawal. Optimal encapsulation occurs, when the perturbed interface is kept stable and transition to viscous entrainment is prevented. Simulations were validated with experimental data. Contrary to previous studies that simplify the problem by approximating the tubes as a doublet of a point mass source and sink, the model presented here employs a detailed geometry. Numerical results shed light on the dependence of the shape of the interface on flow, geometry and physical parameters. These observations can contribute to the design of encapsulation apparatuses considering polydispersity in size and the different shape of the islets.
Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-21 Karolis Jankauskas, Lazaros G. Papageorgiou, Suzanne S. Farid
The previous research work in the literature for capacity planning and scheduling of biopharmaceutical manufacture focused mostly on the use of mixed integer linear programming (MILP). This paper presents fast genetic algorithm (GA) approaches for solving discrete-time MILP problems of capacity planning and scheduling in the biopharmaceutical industry. The proposed approach is validated on two case studies from the literature and compared with MILP models. In case study 1, a medium-term capacity planning problem of a single-site, multi-suite, multi-product biopharmaceutical manufacture is presented. The GA is shown to achieve the global optimum on average 3.6 times faster than a MILP model. In case study 2, a larger long-term planning problem of multi-site, multi-product bio-manufacture is solved. Using the rolling horizon strategy, the GA is demonstrated to achieve near-optimal solutions (1% away from the global optimum) as fast as a MILP model.
Enhancing the performance of a solar-assisted adsorption chiller using advanced composite materials Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-19 Saket Sinha, Dia Milani, Minh Tri Luu, Ali Abbas
Supply-demand Pinch based Methodology for Multi-period Planning under Uncertainty in Components Qualities with Application to Gasoline Blend Planning Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-19 Mahir Jalanko, Vladimir Mahalec
Utilizing Big Data for Batch Process Modeling and Control Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-18 Abhinav Garg, Prashant Mhaskar
This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter’s superior ability to achieve desired PSD shape is demonstrated.
Multi-objective Optimisation for Biopharmaceutical Manufacturing under Uncertainty Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-18 Songsong Liu, Lazaros G. Papageorgiou
This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including the resin selection at each chromatography step, the number of columns, the column diameters and bed heights, and the number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted.
A Kriging-based approach for conjugating specific dynamic models into whole plant stationary simulations Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-17 Roymel. R. Carpio, Felipe. F. Furlan, Roberto. C. Giordano, Argimiro. R. Secchi
Steady-state simulators are usually applied for design, techno-economic analysis and optimization of industrial processes. However, sometimes dynamic systems are important parts of the process, which cannot be disregarded. Coupling a dynamic model within a full-plant for steady-state simulation is a challenging task, whatever might be the simulator concept, either sequential or equation-oriented. An alternative to solve this problem is the use of surrogate models to substitute specific dynamic models, by taking the variable time as an extra input of the meta-model. This methodology was applied in an equation-oriented simulator (EMSO) by the use of Kriging meta-models. A case study involving the production of bioethanol from sugarcane was used to demonstrate the capability of this approach. A Kriging meta-model used to substitute the kinetic model of an enzymatic hydrolysis reactor was conjugated into the global plant simulation and an optimization problem was successfully solved.
Extended cross decomposition for mixed-integer linear programs with strong and weak linking constraints Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-18 Emmanuel Ogbe, Xiang Li
Large-scale mixed-integer linear programming (MILP) problems, such as those from two-stage stochastic programming, usually have a decomposable structure that can be exploited to design efficient optimization methods. Classical Benders decomposition can solve MILPs with weak linking constraints (which are decomposable when linking variables are fixed) but not strong linking constraints (which are not decomposable even when linking variables are fixed). In this paper, we first propose a new rigorous bilevel decomposition strategy for solving MILPs with strong and weak linking constraints, then extend a recently developed cross decomposition method based on this strategy. We also show how to apply the extended cross decomposition method to two-stage stochastic programming problems with conditional-value-at-risk (CVaR) constraints. In the case studies, we demonstrate the significant computational advantage of the proposed extended cross decomposition method as well as the benefit of including CVaR constraints in stochastic programming.
Optimization and Control of a Thin Film Growth Process: A Hybrid First Principles/Artificial Neural Network Based Multiscale Modelling Approach Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-18 Donovan Chaffart, Luis A. Ricardez-Sandoval
This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.
Strategic decision-making in the pharmaceutical industry: a unified decision-making framework Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-15 Catarina M. Marques, Samuel Moniz, Jorge Pinho de Sousa
The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed.This study extends the authors’ previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that “maximizes” productivity while considering the decision-maker preferences.An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that “maximize” productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved.
On improving the hydrogen and methanol production using an auto-thermal double-membrane reactor: Model prediction and optimisation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-13 Hamid Rahmanifard, Reza Vakili, Tatyana Plaksina, Mohammad Reza Rahimpour, Masoud Babaei, Xiaolei Fan
LEAPS2: Learning based Evolutionary Assistive Paradigm for Surrogate Selection Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-11 Sushant S. Garud, Iftekhar A. Karimi, Markus Kraft
We propose a learning-based paradigm (LEAPS2) to recommend the best surrogate/s with minimal computational effort using the input-output data of a complex physico-numerical system. Emulating the knowledge pyramid, LEAPS2 uses several attributes to extract system information from the data, correlates them with surrogate performances, stores this attribute-surrogate knowledge in a regression tree ensemble, and uses the ensemble to recommend surrogates for unknown systems. We implement LEAPS2 using data from 66 diverse analytical functions, 18 attributes, and 25 surrogates. By progressively adding data, we demonstrate that LEAPS2 learns to improve computational efficiency and functional accuracy. Besides, the architecture of LEAPS2 enables its evolution via more attributes and surrogates. We employ LEAPS2 to recommend surrogates for estimating the bubble and dew point temperatures of LNG. Interestingly, our assistive tool suggests a different surrogate for each temperature, and hints that DPT may be harder to approximate than BPT.
Optimal Laypunov Exponent Parameters for Stability Analysis of Batch Reactors with Model Predictive Control Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-08 Walter Kähm, Vassilios S. Vassiliadis
A Total Site Synthesis approach for the selection, integration and planning of multiple-feedstock biorefineries Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-08 Konstantinos A Pyrgakis, Professor Antonios Kokossis
A Framework for Model Reliability and Estimability Analysis of Crystallization Processes with Multi-Impurity Multi-Dimensional Population Balance Models Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-08 D. Fysikopoulos, B. Benyahia, A. Borsos, Z.K. Nagy, C.D. Rielly
Exo-parametric (“Inside-Out”) Model of Discounted Cash Flow Calculations Using NPV%: Macro Calculation of Coefficients for an Exact, Collapsed Financial Model Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-07 Duncan A. Mellichamp
In a 2013 paper,1 the author developed an internally-consistent discounted cash flow (DCF) model that represents the key financial outputs of a conceptual chemical process design using just four variables:•Total Investment, TI (capital required to construct and operate a project for NTotal years),•ROIBT and NPVproj (the traditional annual and long-term (NPV) profitabilities), and•NPV% [a new metric whose reciprocal, (NPV%)−1, directly expresses short-term project risk].A traditional spreadsheet relating these four dependent long- and short-term financial metrics to a project's two independent design variables, PBT and FC, is functionally dependent on the many process design parameters (tax rate, discount factor, etc.) and highly complicated, involving non-linear (mostly geometric) relationships. Surprisingly, an exact linear “inner model” including the usual nonlinear relation for ROIBT is obtained by collapsing the original large-scale financial model (the complicated spreadsheet) using these four dependent variables, if the coefficients are functions of fixed design parameters, as when held constant in design. The power/utility to understand several key features of a project's design financial characteristics are revealed via the “inner model” through this reversal of the usual modeling hierarchy. The “outer model” coefficients incorporate the highly non-linear DCF functionalities. The new form is referred to as “exo-parametric” or an “inside-out” model.
A Mixed-Integer Conic Programming Formulation for Computing the Flexibility Index under Multivariate Gaussian Uncertainty Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-09-05 Joshua L. Pulsipher, Victor M. Zavala
We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility index by solving a mixed-integer conic program (MICP). This methodology directly characterizes ellipsoidal sets to capture correlations in contrast to previous methodologies that employ approximations. We also show that, under a Gaussian representation, the flexibility index can be used to obtain a lower bound for the so-called stochastic flexibility index (i.e., the probability of having feasible operation). Our results also show that the methodology can be generalized to capture different types of uncertainty sets.
Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-31 Felipe Scott, Pamela Wilson, Raúl Conejeros, Vassilios S. Vassiliadis
This work presents a novel, differentiable, way of solving dynamic Flux Balance Analysis (dFBA) problems by embedding flux balance analysis of metabolic network models within lumped bulk kinetics for biochemical processes. The proposed methodology utilizes transformation of the bounds of the embedded linear programming problem of flux balance analysis via a logarithmic barrier (interior point) approach. By exploiting the first-order optimality conditions of the interior-point problem, and with further transformations, the approach results in a system of implicit ordinary differential equations. Results from four case studies, show that the CPU and wall-times obtained using the proposed method are competitive with existing state-of-the art approaches for solving dFBA simulations, for problem sizes up to genome-scale. The differentiability of the proposed approach allows, using existing commercial packages, its application to the optimal control of dFBA problems at a genome-scale size, thus outperforming existing formulations as shown by two dynamic optimization case studies.
A parallel unidirectional coupled DEM-PBM model for the efficient simulation of computationally intensive particulate process systems Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-30 Chaitanya Sampat, Franklin Bettencourt, Yukteshwar Baranwal, Ioannis Paraskevakos, Anik Chaturbedi, Subhodh Karkala, Shantenu Jha, Rohit Ramachandran, Marianthi Ierapetritou
The accurate modeling of the physics underlying particulate processes is complicated and requires significant computational capabilities to solve using particle-based models. In this work, a unidirectional multi-scale approach was used to model the high shear wet granulation process. A multi-dimensional population balance model (PBM) was developed with a mechanistic kernel, which in turn obtained collision data from the discrete element modeling (DEM) simulation. The PBM was parallelized using a hybrid OpenMP+MPI approach. The DEM simulations were performed using LIGGGHTS, which was parallelized using MPI. Speedups of approximately 14 were obtained for the PBM simulations and approximately 12 for the DEM simulations. The uni-directional coupling of DEM to PBM was performed using middle-ware components (RADICAL-Pilot) that did not require modifications of the DEM or PBM codes, yet supported flexible execution on high-performance platforms. Results demonstrate scaling from 1 to 128 cores for the PBM and up to 256 cores for the DEM. The proposed method, implementations and middle-ware enable the modeling of high shear wet granulation process faster than existing approaches in literature.
Using Correlation Based Adaptive LASSO Algorithm to Develop QSPR of Antitumour Agents for DNA-Drug Binding Prediction Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-30 Shounak Datta, Vikrant A. Dev, Mario R. Eden
In the United States, cancer is the second leading cause of death. Worldwide too, cancer is a major health problem. Hence, treatment of cancerous tumors remains a matter of very high concern. Apart from surgical treatment, the most commonly employed treatment is chemotherapy. But, due to long-term side effects such as organ damage and loss of teeth, doctors and patients are interested in treatments with reduced side effects. So far, a reasonably acceptable alternative to chemotherapy has not emerged. Recently, 9-anilinoacridines were evaluated as potential antitumor agents due to their enhanced tendency of DNA binding. For an initial evaluation of the drug performance, the association constant, K, is considered to be the key DNA drug binding property. In our work, to reduce experimental efforts and the associated chemical footprint, we develop a QSPR to model K. In our work, to model K, we utilized descriptors requiring representation of molecular structures in two dimensions or less. To establish a relationship between the descriptors and K, we have developed a correlation based adaptive LASSO algorithm (CorrLASSO). CorrLASSO, like LASSO (least absolute shrinkage and selection operator), incorporates feature selection as part of the learning procedure. Also, it is useful for dealing with high-dimensional data. As an improvement, CorrLASSO evaluates correlation between descriptors/features and the dependent property to generate a model with high performance metrics. In our work, R2, Q2 and MSE (Mean Square Error) were utilized as performance metrics.
Incorporation of Heuristic Knowledge in the Optimal Design of Formulated Products: Application to a Cosmetic Emulsion Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-30 Javier A. Arrieta-Escobar, Fernando P. Bernardo, Alvaro Orjuela, Mauricio Camargo, Laure Morel
In particular industrial sectors, such as the cosmetic, there is a considerable amount of heuristics during the formulation stage, namely regarding qualitative function of ingredients, their incompatibilities, synergies and antagonisms, as well as their impact on sensorial attributes. In this work, the heuristic knowledge around the formulation of cosmetics emulsions has been incorporated into a systematic CAMD methodology. The methodology was tested in the creation of rinse-off hair conditioners. From an initial list of ingredients (i.e. twenty-four emollients, six thickeners and five emulsifiers) and a set of heuristics for their incorporation into cosmetic formulations, a group of optimized alternatives under specific performance and economic targets was obtained. Nine of the resulting formulations were prepared and then analyzed using instrumental methods. The rheological, textural and microstructural characteristics were similar for most of the samples, confirming the potential of this proposed methodology in designing and tailoring formulated products.
A Multi-Objective Robust Optimization Scheme for Reducing Optimization Performance Deterioration Caused by Fluctuation of Decision Parameters in Chemical Processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-28 Liao Zhiqiang, Li Taifu, Chen Peng, Zuo Shilun
The fluctuation of decision parameters will deviate from the optimal decision, which will have significant impact on the optimization performance of chemical processes. To reduce optimization performance deterioration caused by fluctuation of decision parameters in chemical processes, a multi-objective robust optimization scheme is developed to assess performance robustness. In addition, based on the model that maps decision parameters to objective performance through neural network, a new robustness evaluation metric is created as the fitness value of the multi-objective evolutionary algorithm (for improving the strength Pareto evolutionary algorithm (SPEAII)) to elaborate the relationship between robustness and fluctuation. The efficacy of the proposed method is verified with HCN production process application by comparing with genetic algorithm (GA) and weighted single-objective robust optimization.
Dynamic Real-Time Optimization of Distributed MPC Systems Using Rigorous Closed-Loop Prediction Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-28 Hao Li, Christopher L.E. Swartz
A dynamic real-time optimization (DRTO) formulation with closed-loop prediction is used to coordinate distributed model predictive controllers (MPCs) by rigorously predicting the interaction between the distributed MPCs and full plant response in the DRTO formulation. This results a multi-level optimization problem that is solved by replacing the MPC quadratic programming subproblems by their equivalent Karush-Kuhn-Tucker (KKT) first-order optimality conditions to yield a single-level mathematical program with complementarity constraints (MPCC). The proposed formulation is able to perform both target tracking and economic optimization with significant performance improvement over decentralized control, and similar performance to centralized MPC. A linear dynamic case study illustrates the performance of the proposed strategy for coordination of distributed MPCs for different levels of plant interaction. The method is thereafter applied to a nonlinear integrated plant with recycle, where its performance in both set-point target tracking and economic optimization is demonstrated.
Benchmarking ADMM in Nonconvex NLPs Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-27 Jose S. Rodriguez, Bethany Nicholson, Carl Laird, Victor M. Zavala
We study connections between the alternating direction method of multipliers (ADMM), the classical method of multipliers (MM), and progressive hedging (PH). The connections are used to derive benchmark metrics and strategies to monitor and accelerate convergence and to help explain why ADMM and PH are capable of solving complex nonconvex NLPs. Specifically, we observe that ADMM is an inexact version of MM and approaches its performance when multiple coordination steps are performed. In addition, we use the observation that PH is a specialization of ADMM and borrow Lyapunov function and primal-dual feasibility metrics used in ADMM to explain why PH is capable of solving nonconvex NLPs. This analysis also highlights that specialized PH schemes can be derived to tackle a wider range of stochastic programs and even other problem classes. Our exposition is tutorial in nature and seeks to to motivate algorithmic improvements and new decomposition strategies.
Surrogate model generation using self-optimizing variables Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-25 Julian Straus, Sigurd Skogestad
This paper presents the application of self-optimizing concepts for more efficient generation of steady-state surrogate models. Surrogate model generation generally has problems with a large number of independent variables resulting in a large sampling space. If the surrogate model is to be used for optimization, utilizing self-optimizing variables allows to map a close-to-optimal response surface, which reduces the model complexity. In particular, the mapped surface becomes much “flatter”, allowing for a simpler representation, for example, a linear map or neglecting the dependency of certain variables completely. The proposed method is studied using an ammonia reactor which for some disturbances shows limit-cycle behaviour and/or reactor extinction. Using self-optimizing variables, it is possible to reduce the number of manipulated variables by three and map a response surface close to the optimal response surface. With the original variables, the response surface would include also regions in which the reactor is extinct.
Crystallization of Calcium Carbonate and Magnesium Hydroxide in the Heat Exchangers of Once-through Multistage Flash (MSF-OT) Desalination Process Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-25 Salih Alsadaie, Iqbal M. Mujtaba
In this paper, a dynamic model of fouling is presented to predict the crystallization of calcium carbonate and magnesium hydroxide inside the condenser tubes of Once-Through Multistage Flash (MSF-OT) desalination process. The model considers the combination of kinetic and mass diffusion rates taking into account the effect of temperature, velocity and salinity of the seawater. The equations for seawater carbonate system are used to calculate the concentration of the seawater species. The effects of salinity and temperature on the solubility of calcium carbonate and magnesium hydroxide are also considered. The results reveal an increase in the fouling inside the tubes caused by crystallization of CaCO3 and Mg(OH)2 with increase in the stage temperature. The intake seawater temperature and the Top Brine Temperature (TBT) are varied to investigate their impact on the fouling process. The results show that the (TBT) has greater impact than the seawater temperature on increasing the fouling.
Modeling and Control of Battery Systems. Part I: Revisiting Butler-Volmer Equations to Model Non-linear Coupling of Various Capacity Fade Mechanisms Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-24 Resmi Suresh, Raghunathan Rengaswamy
Lithium-ion batteries, affected by various capacity fade mechanisms, require an efficient battery management system that can prolong battery lifetime by periodic diagnosis, subsequent management and control. The work presented in this two-part paper is an investigation and development of strategies for battery modeling and controller implementation, which are two of the essential components of any battery management system. In this first part, a generalized approach to incorporate non-linear coupling of various capacity fade mechanisms is proposed. Though there exist numerous models to capture effects of various capacity fade mechanisms, they fail to model non-linear coupling as they assume linear superposition of individual current densities (provided by individual Butler-Volmer equations). Considering battery as a system with multiple reactions (both desired and undesired reactions), rate equations can be written for the overall system. Re-deriving a single Butler-Volmer equation from this rate equation provided insights regarding the true nature of coupling between various reactions inside a battery. Incorporating various side reactions using this framework to a detailed ideal battery model would help in understanding the behavior of a battery with aging and this information can be useful to diagnose various problems in the battery. For demonstrating the implementation and usefulness of this approach, SEI layer formation and Li plating are incorporated to a detailed battery model in this article.
Modeling and Control of Battery Systems. Part II: A Model Predictive Controller for Optimal Charging Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-24 Resmi Suresh, Raghunathan Rengaswamy
In this part of the paper, a control strategy for optimal charging is discussed. This work seeks to develop a capacity fade minimizing model predictive control (MPC) framework, which can help in identification and realization of optimum charge-discharge cycles in Lithium-ion (Li-ion) batteries. Although the model developed in the first part is a good representation for a battery, it has limitations for on-line applications due to its complexity. For on-line applications, it is important that the model is computationally fast, but at the same time incorporate the effects of various capacity fade mechanisms. Development of a simple lumped model to meet these requirements is also a part of this work.
Integration of the Biorefinery Concept for the Development of Sustainable Processes for Pulp and Paper Industry Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-24 Ghochapon Mongkhonsiri, Rafiqul Gani, Pomthong Malakul, Suttichai Assabumrungrat
Development of a Plant-Wide Dimethyl Oxalate (DMO) Synthesis Process from Syngas: Rigorous Design and Optimization Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-23 Bor-Yih Yu, Chuan-Yi Chung, I-Lung Chien
An Efficient Polynomial Chaos Expansion Strategy for Active Fault Identification of Chemical Processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-22 René Schenkendorf, Xiangzhong Xie, Ulrike Krewer
This paper is concerned with a highly efficient active fault detection and isolation (FDI) framework. An auxiliary, fault-revealing input is derived by solving an optimization problem. As we implement a model-based approach, the active FDI framework is robustified against model parameter uncertainties, including parameter correlations which are common for experimentally derived parameters. Moreover, critical safety limits are considered, and an optimal process performance is fulfilled in parallel. In this work, which is an extension to our ESCAPE-2017 contribution, a novel highly effective polynomial chaos expansion (PCE) approach is used to address parameter uncertainties and to include process design parameters directly. To reduce the computational load, we combined the PCE with a least angle regression (LAR) strategy. The overall effectiveness of the novel one-shot sparse polynomial chaos expansion (OS2-PCE) concept is demonstrated by analyzing a tubular plug flow reactor illustrating the need for uncertainty and parameter correlation analysis in FDI while ensuring an optimal and safe process operation, respectively.
Simultaneous heat integration and economic optimization of the coal-to-SNG process Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-22 Bo Huang, Rui Gao, Jianliang Xu, Zhenghua Dai, Fuchen Wang
The area of the heat recovery network has a significant impact on the economic performance of the coal-to-SNG process. This paper proposes a superstructure of the methanation unit, and the number of the methanator, recycle gas extraction position (RGEXP), as well as the operating conditions, are optimized. Simultaneous optimization and heat integration are performed along with the area targeting to weigh against the operating cost and capital cost of the coal-to-SNG process. Area cost is evaluated by assuming vertical heat transfer between cold and hot composite curves. A sequential method is used to provide initial values for the simultaneous model. Results show that Case7 (2) has the best economic performance, which has 7 methanators and recycled gas is extracted from the 2nd methanator. The total annual cost of Case7 (2) can be reduced by 10.36 MM$•a−1, and exergy efficiency can be also improved by 0.77% compared with an industrial plant.
An Optimization Framework for Scheduling of Converter Aisle Operation in a Nickel Smelting Plant Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-18 Christopher M. Ewaschuk, Christopher L.E. Swartz, Yale Zhang
The scheduling of converter aisle operation in a nickel smelting plant is a complex task with significant ramifications to plant profitability and production. An optimization-based scheduling formulation is developed using a continuous-time paradigm to accurately represent event timings. The formulation accounts for environmental restrictions on sulfur dioxide emissions, using event timing constraints. Flash furnaces are characterized by a continuous inlet flow and intermittent, discrete material removal, which is captured via novel semi-continuous modeling. An innovative sequencing and symmetry-breaking scheme is introduced to exploit identical units operating in parallel. A rolling horizon feature is included to accommodate real-time optimization. Tightening constraints are developed to improve the computational performance. A unique, multi-tiered procedure enhances the practicality of the solution and supports additional operability objectives, without compromising the optimality of the primary objective. The success of the approach is demonstrated via case studies arising from industrial production scenarios.
Computer-aided molecular product-process design under property uncertainties – A Monte Carlo based optimization strategy Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-19 Jérôme Frutiger, Stefano Cignitti, Jens Abildskov, John M. Woodley, Gürkan Sin
A methodology is presented to solve a computer-aided molecular design (CAMD) and process design model problems under consideration of fluid property uncertainty. The uncertainties of the group contribution (GC) property prediction models are quantified for which asymptotic approximation of the covariance of parameter estimation errors is performed following a regression analysis. A Monte Carlo sampling technique generates GC factor samples within the respective uncertainties, which are evaluated separately as constraints to the CAMD optimization problem. The methodology is applied to identify working fluid candidates for an organic Rankine cycle used as waste heat recovery system in a marine diesel engine. CAMD under property uncertainties allows 1) identifying robust and more reliable molecules with respect to property uncertainties (conservative approach) and 2) enhancing the search space in order to find potentially globally optimal working fluids (optimistic approach). Suitable Hydrofluoroolefins (HFO) have been identified as potential working fluids for waste heat recovery.
Systematic Approach of Multi-Label Classification for Production Scheduling Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-19 Edrisi Muñoz, Elisabet Capón-Garcia
Process scheduling problems have been largely studied in the literature, and a large number of methods and approaches are available and capable of solving them. However, the selection of the best method fitting to a real practical problem at hand, and the limited number of experts in optimization and operations research within industrial environments seriously limit the practical application of the theoretical methods. This work proposes a supporting framework based on a multi-label classification strategy, for selecting those mathematical scheduling models that are more suitable to solve a certain scheduling problem definition. We have decomposed the problem description into binary classification problems, in order to analyze the convenience of each scheduling model for a certain definition. As a result, a systematic approach to scheduling model selection is achieved, facilitating the bridge between theoretical developments and industrial practice.
Effect of Feed Composition on Cryogenic Distillation Precooling Configurations Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-18 William L. Luyben
Precooling the feed of cryogenic distillation columns is achieved by using the cold distillate and bottoms streams. The distillate is typically removed from the top of the reflux drum as a vapor to reduce the condenser refrigeration heat duty. The bottoms stream is typically a liquid at a somewhat higher temperature than the distillate. The colder vapor distillate can only provide sensible heat removal. The warmer bottoms liquid stream can provide more heat removal because of vaporization. The purpose of this paper is to demonstrate that the appropriate arrangement of the precooling heat exchangers depends on the column feed composition. If the feed contains little light key component, the bottoms stream is large and the feed can be easily precooled. If the feed contains little heavy key component, the bottoms stream is small and precooling the feed is more difficult.
Robust design of ambient-air vaporizer based on time-series clustering Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-18 Yongkyu Lee, Jonggeol Na, Won Bo Lee
A methodology for the robust design of an ambient-air vaporizer under time-series weather conditions is proposed. Two techniques are used to extract representative features in the time-series data. (i) The major trend of a day is rapidly identified by the discrete wavelet transform (DWT), in which a high level of Haar function reflects the trend of a day and drastically reduces the data size. (ii) The k-means clustering method groups the similar features of a year, and the reconstructed time-series dataset extracted by the centroids of clusters represents the weather conditions of a year. The results of the multi-feature-based optimization were compared with non-wavelet based and multi-period optimization by simulation under a year of data. The design structure from the feature extraction shows 22.92% better performance than the original case and is 12 times more robust in different weather conditions than clustering with raw data.
Semi-batch Industrial Process of Nitriles Production: Dynamic Simulation and Validation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-08-16 K.D. Brito, S.F. Vasconcelos, G.F. Farias Neto, A.S. Damasceno, M.F. Figueirêdo, W.B. Ramos, R.P. Brito
A semi-batch reactor is used to produce nitriles by reacting fatty acids and ammonia. Despite it is an old chemical process and many patents are available in the literature, its mathematical model was not developed until now. This process comprises a semi-batch reactor with recycle, which makes the numerical solution a challenge to face. In this paper, the entire process has been computer simulated for the very first time. Aspen Plus software was used to find the initial condition for the set of differential algebraic equations, which will be solved by Aspen Plus Dynamics, the key tool to ensure that the entire batch cycle could be properly programmed. Besides that, PI controllers were implemented to guarantee normal and safety operation of the process evaluated, in other words, controllers performed well in tracking pressure and composition profiles, target conversion for the desired product and level behavior. The simulated results were validated with industrial data and opportunities for improving the process were evaluated. The main result was a reduction in batch cycle time for around 40 minutes.
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