An ontology framework towards decentralized information management for eco-industrial parks Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-18 Li Zhou, Chuan Zhang, Iftekhar A. Karimi, Markus Kraft
In this paper, we develop a skeletal ontology for eco-industrial parks. A top-down conceptual framework including five operating levels (unit operations, processes, plants, industrial resource networks and eco-industrial parks) is employed to guide the design of the ontology structure. The detailed ontological representation of each level is realized through adapting and extending OntoCAPE, an ontology of the chemical engineering domain. Based on the proposed ontology, a framework for distributed information management is proposed for eco-industrial parks. As an example, this ontology is used to create a knowledge base for Jurong Island, an industrial park in Singapore. Its potential uses in supporting process modeling and optimization and facilitating industrial symbiosis are also discussed in the paper.
A Dynamical Model of an Aeration Plant for Wastewater Treatment using a Phenomenological based Semi-physical Modeling Methodology Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-18 C. Zuluaga-Bedoya, M. Ruiz-Botero, M. Ospina-Alarcón, J. Garcia-Tirado
Diffused aeration is a sensitive process for wastewater treatment. Because of the nonlinearity and complexity of aerator dynamics due to microorganism metabolism and oxygen transfer, reliable mathematical models are needed to perform control-oriented tasks. To this end, in this study we develop a Phenomenological based Semi-physical Model (PBSM) to predict and describe the dynamic behavior of the oxygen transfer in a diffused aeration process by means of a formal modeling methodology. This model will then be validated by using data from an aeration pilot plant. In this paper, we also show a lack of agreement in the literature in terms of the different available ways to represent the volumetric oxygen transfer coefficient kLa. Reasonable agreement between the developed model and plant data is found by considering a phenomenological approach of the kLa instead of considering many of the available empirical correlations in the literature.
Enhanced surrogate assisted framework for constrained global optimization of expensive black-box functions Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-17 Roymel R. Carpio, Roberto C. Giordano, Argimiro R. Secchi
An enhanced surrogate assisted framework, based on Probability of Improvement (PI) method, is proposed in this paper. We made some modifications to the original PI approach to enhance the performance of the modeling and optimization framework, leading to fewer rigorous simulations to find the optimal solution without loss of accuracy. We also extended the algorithm for handling general constraints using a fully probabilistic approach. The behavior of the proposed framework was investigated through a set of9 Unconstrained Test Functions (UTF),7 Constrained Optimization Problems (COP) and3 Chemical Engineering Problems (CEP). The numerical results indicate that a lower number of rigorous model simulations were needed for optimizing UTF compared to the classic PI method and that the proposed framework was capable of achieving sustained near optimal solutions for COP and CEP. These results indicate that the proposed framework is suitable for solving computationally expensive constrained black-box optimization problems.
Mixed-integer nonlinear programming models for optimal design of reliable chemical plants Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-09-01 Yixin Ye, Ignacio E. Grossmann, Jose M. Pinto
Motivated by reliability/availability concerns in chemical plants, this paper proposes MINLP models to determine the optimal selection of parallel units considering the trade-off between availability and cost. Assuming an underlying serial structure for availability, we consider first a case where the system transitions between available and unavailable states, and second the case with an intermediate state at half capacity. Two non-convex MINLP models maximizing net profit are introduced for the two cases. In addition, a bi-criterion MINLP model is proposed to maximize availability and to minimize cost for the first case. It is shown that the corresponding epsilon-constrained model, where the availability is maximized subject to parametrically varying upper bound of the cost, can be reformulated as a convex MINLP. Availability is also incorporated in the superstructure optimization of process flowsheets. The performances of the proposed models are illustrated with a methanol synthesis and a toluene hydrodealkylation process.
Computer aided chemical product design – ProCAPD and tailor-made blended products Comput. Chem. Eng. (IF 3.113) 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.
Reactive scheduling of crude oil using structure adapted genetic algorithm under multiple uncertainties Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-04-03 Debashish Panda, Manojkumar Ramteke
Crude oil processed in marine access refineries contributes about 15% of the total energy production worldwide. An optimized schedule of crude unloading and charging in these offers the best utilization of available resources to increase the profitability and also helps in incorporating the future uncertainties commonly encountered in the operation. In the present study, a new reactive crude oil scheduling methodology is developed for marine-access refinery using a structured adapted genetic algorithm to handle the commonly encountered uncertainties of increase in demand and ship arrival delay. Three different industrial examples with 21, 21 and 42 periods are solved for above uncertainties with single and multiple objectives. In the single-objective formulation, profit is maximized whereas in multi-objective formulation an additional objective of inter-period deviation in crude flow to distillation units is minimized. The results obtained show the efficient handling of uncertainties with improved profitability and operability of the plant.
Multi-parametric mixed integer linear programming under global uncertainty Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-04-18 Vassilis M. Charitopoulos, Lazaros G. Papageorgiou, Vivek Dua
Major application areas of the process systems engineering, such as hybrid control, scheduling and synthesis can be formulated as mixed integer linear programming (MILP) problems and are naturally susceptible to uncertainty. Multi-parametric programming theory forms an active field of research and has proven to provide invaluable tools for decision making under uncertainty. While uncertainty in the right-hand side (RHS) and in the objective function’s coefficients (OFC) have been thoroughly studied in the literature, the case of left-hand side (LHS) uncertainty has attracted significantly less attention mainly because of the computational implications that arise in such a problem. In the present work, we propose a novel algorithm for the analytical solution of multi-parametric MILP (mp-MILP) problems under global uncertainty, i.e. RHS, OFC and LHS. The exact explicit solutions and the corresponding regions of the parametric space are computed while a number of case studies illustrates the merits of the proposed algorithm.
Semantically-enabled repositories in multi-disciplinary domains: The case of biorefineries Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-04-20 Eirini Siougkrou, Filopoimin Lykokanellos, Foteini Barla, Antonis C. Kokossis
There is an increased use of problem representations (i.e. superstructures in synthesis problems; networks in route problems; graphs; ordered graphs in various systems representations) following on significant advances in optimization technologies that hold capabilities to solve, robustly, large-scale problems. In an attempt to systematically tackle disparate domains and build high-throughput functions, the paper contributes with a semantically-enabled approach systematized and engineered by ontologies. The aim is to develop an intelligent environment with capabilities to build and scale-up system representations, automatically. The work is demonstrated on problems akin to biorenewables and biorefineries; an identical approach is possible to the general problem. Using relations and rules defined among entities, semantics are deployed to model and expand domains (biorefinery pathways) whereas enabling extracting and creating knowledge. The repository, already on a web-based platform and available as open-source, essentially upgrades conventional representations with capabilities to share (import/export) and integrate its content externally.
Batch-centric scheduling formulation for treelike pipeline systems with forbidden product sequences Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-04-25 Pedro M. Castro, Hossein Mostafaei
This paper presents a new mixed-integer linear programming (MILP) formulation for the short-term scheduling of liquid transportation by pipeline. It relies on a continuous-time representation featuring a single grid and is suitable for treelike systems with one input node simultaneously feeding multiple output nodes. The novelty is the incorporation of constraints for rigorously enforcing forbidden product sequences and ensuring a single batch inside idle segments. We tackle a real-life problem from the Iranian pipeline network, generating an optimal schedule that increases pipeline capacity by 6.2%, meeting demand from the weekly plan, fourteen hours earlier. We also solve three benchmark problems from the literature. The results show a significantly better performance than a closely related, state-of-the-art, product-centric formulation, which is unable to find a feasible solution for a problem that can now be solved to proven global optimality. It is primarily because multiple batches can enter/leave segments during a time slot.
Deviation propagation analysis along a cumene process by using dynamic simulations Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-05 Murillo Carlos, Berdouzi Fatine, Olivier-Maget Nelly, Gabas Nadine
The dynamic response of benzene alkylation process to a set of deviations is analyzed with Aspen Plus Dynamics. A quantitative risk assessment is developed through simulations of deviation scenarios. The process comprises a reactor and three distillation columns with a recycle stream. The simulation scenarios are determined according to lessons learnt from accidents. This study underlines the conditions that induce an overpressure or a flooding in a distillation column. Three scenarios are proposed: feed flowrate variations, coolant flowrate reduction and cooling of the reboiler steam. Thereafter, the results allow calculating a set of risk indexes related to flooding and overpressure phenomena. This study underlines the deviation propagation effects that can be expected in all the process equipment. Moreover, it represents a significant contribution to the definition of the process control strategy and the necessary safety barriers.
Dynamic self-optimizing control for unconstrained batch processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-06 Lingjian Ye, Sigurd Skogestad
In this paper, we consider near-optimal operation for a class of unconstrained batch processes using the self-optimizing control (SOC) methodology. The existing static SOC approach is extended to the dynamic case by means of a static reformulation of the dynamic optimization problem. However, the dynamic SOC problem is posed as a structure-constrained controlled variable (CV) selection problem, which is different from the static cases. A lower-block triangular structure is specified for the combination matrix, H, to allow for optimal operation whilst respecting causality. A new result is that the structure-constrained SOC problem still results in a convex formulation, which has an analytic solution where the optimal CVs associated with discrete time instants are solved separately. In addition, the inputs are directly determined based on current CV functions for on-line utilization. A fed-batch reactor and a batch distillation column are used to demonstrate the usefulness of the proposed approach.
Dynamic Graph Embedding for Fault Detection Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-06 Haitao Zhao
Using sequence information can improve performances in fault detection for serial (temporal) correlated process data. Classical methods firstly construct extended vectors through concatenating current process data and a certain number of previous process data, and then take dimension reduction methods. However, the simple extension of process data may distort the correlation between variables and largely increase the dimensionality. This paper proposes a novel algorithm, called Dynamic Graph Embedding (DGE), for fault detection. DGE adopts augmented matrices instead of extended vectors to encode sequence information. Furthermore, DGE incorporates both time information and neighborhood information to form similarities of different process data. And then DGE is designed to obtain embedding matrices with Markov chain analysis of the similarities. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of DGE in terms of missed detection rate (MDR) and false alarm rate (FAR).
A Biologically-Inspired Approach for Adaptive Control of Advanced Energy Systems Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-06 Gaurav Mirlekar, Ghassan Al-Sinbol, Mario Perhinschi, Fernando V. Lima
In this article, a novel approach is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIO-CS) with an Artificial Neural Network (ANN)-based adaptive component for advanced energy systems applications. Specifically, BIO-CS employs gradient-based optimal control solvers in a biologically-inspired manner, following the rule of pursuit for ants, to simultaneously control multiple process outputs at their desired setpoints. Also, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems. The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. In particular, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIM® is addressed. The proposed control laws are derived in MATLAB®, while the plant models are built in DYNSIM®, and a previously developed MATLAB®-DYNSIM® link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking. The proposed framework thus provides a promising alternative for advanced control of energy systems of the future.
Optimal molecular design towards an environmental friendly solvent recovery process Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-06 Jecksin Ooi, Denny K.S. Ng, Nishanth G. Chemmangattuvalappil
This paper presents a novel Computer Aided Molecular Design (CAMD) methodology for the design of solvents with consideration of environmental impact during the extraction and recovery processes. Throughout the years, CAMD techniques have been widely applied to design novel molecules for various applications, especially solvents. Most of the works in this area are only focusing on designing solvents that can achieve its functionality. However, limited works consider safety, health and environmental impacts of the process in which the solvent is applied. In this work, a quantitative assessment of the total environmental burden for solvent recovery process is incorporated into a single stage CAMD framework. The CAMD formulation includes molecular properties that affect the quantitative assessment of the environmental impact of a process. A multi-objective solvent design framework is then solved using Fuzzy Analytic Hierarchy Process (FAHP) weighting approach to design solvents that satisfy various target properties. With this approach, the generated solvents can simultaneously improve the overall environmental characteristic of a process and give a better balance of performance for a set of predefined properties. To illustrate the proposed methodology, a case study on solvent design for residual oil extraction from palm pressed fibre is presented.
Synthesis of Biogas Supply Networks using various Biomass and Manure Types Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-07 Jafaru Egieya, Lidija Čuček, Klavdija Zirngast, Adeniyi Isafiade, Bojan Pahor, Zdravko Kravanja
This contribution presents a developed generic mixed integer linear programming (MILP) model for optimizing biogas supply network to generate electricity over monthly time-periods by maximizing the economic performance. Dry matter content and methane yield for each feedstock are included, which more accurately represents a realistic network. The model is applied to an illustrative case study of an agricultural biogas production plant in Slovenia. Optimal results show poultry manure and bedding and corn silage as the selected feedstocks to meet the production of 999 kW of electricity. Technologies selected include anaerobic digester, press-based dewatering and combined heat and power plant (CHP), while water, and heat required for the anaerobic digestion plant itself are “recycled”. A profit after tax of about 254,625 $/y is obtained. Furthermore, sensitivity analysis in terms of prices of electricity and digestate, dry matter content and utilization of crop residues is performed.
Economics Optimizing Control of a Multi-Product Reactive Distillation Process under Model Uncertainty Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-07 Daniel Haßkerl, Clemens Lindscheid, Sankaranarayanan Subramanian, Patrick Diewald, Alexandru Tatulea-Codrean, Sebastian Engell
By the use of online optimization, the profitability of chemical processes can be enhanced while meeting process-related, environmental and ecological constraints. Two well-known techniques to achieve an economically optimal operation of chemical processes are repetitive stationary optimization (RTO) combined with advanced control and direct economics optimizing control on a receding horizon (also called one-layer approach or dynamic RTO). Direct economics optimizing control can react faster to disturbances and is better suited for the optimization of transients between different products. On the other hand it is computationally demanding and requires sufficiently accurate dynamic models.In this paper, we discuss economics optimizing control of a very complex process, a multi-product transesterification reaction that is realized in a reactive distillation process. The process model, the specification of the economics optimizing controller, the implementation of the dynamic optimization, and the robustification of the controller by a multi-stage approach are discussed using simulation studies.
LAMOS: a linear algorithm to identify the origin of multiple optimal flux distributions in metabolic networks Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-07 Ehsan Motamedian, Fereshteh Naeimpoor
In flux balance analysis, where flux distribution within a cell metabolic network is estimated by optimizing an objective function, there commonly exist multiple optimal flux distributions. Although finding all optimal solutions is possible, their interpretation is a challenge. A new four-phase algorithm (LAMOS) is therefore proposed in this work to efficiently enumerate all of these solutions based on iterative substitution of a current non-basic variable with a basic variable. These basic and non-basic variables are called key reaction pairs that their successive activity or inactivity causes alternate optimal solutions. LAMOS was implemented on E. coli metabolic models and the results proved it as a simple and fast method capable of finding the key reactions as well as reactions participating in the futile cycles. Key reactions were 1-3% of all reactions for the large-scale models and these reactions were identified using only 1% of optimal solutions.
Improved capacity estimation technique for the battery management systems of electric vehicles using the fixed-point iteration method Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-04 Woosuk Sung, Jaewook Lee
This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design.
Model on Transport Phenomena and Control of Rod Growth Uniformity in Siemens CVD Reactor Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-04 Xue-Gang Li, Wen-De Xiao
The transport phenomena in Siemens reactors has been investigated by using computational fluid dynamics (CFD) technique. The reaction kinetics was validated against two data sets of silicon epitaxial deposition experiments. Comparative simulations were carried out for four types of reactors with different configurations of gas supplying nozzles and offgas ports. The uniformity index was introduced to evaluate rod growth uniformity. The result showed that improper gas distributing system would result in zones of low velocity together with high temperature, and uneven species concentration distribution, finally cause low uniformity of rod growth. The reactor with a single offgas port at the center of the bottom plate was supposed to be best design among the four from both the performance and the equipment complexity points of view.
Optimization of multistage fractured horizontal wells in tight oil based on embedded discrete fracture model Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-01 Shiqian Xu, Qihong Feng, Sen Wang, Farzam Javadpour, Yuyao Li
Development of A Reaction/Distillation Matrix for Systematic Generation of Sequences in A Single Two Component Reaction-Separation Case Study Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-07-02 Mahya Nezhadfard, Leila S. Emami, Norollah Kasiri, Mohammad H. Khanof, Amirhossein Khalili-Garakani, Javad Ivakpour
A Process Design Approach to Manage the Uncertainty of Industrial Flaring during Abnormal Operations Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-30 Monzure-Khoda Kazi, Fadwa Eljack, Mohammad Amanullah, Ahmed AlNouss, Vasiliki Kazantzi
Locality Preserving Discriminative Canonical Variate Analysis for Fault Diagnosis Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-30 Qiugang Lu, Benben Jiang, R. Bhushan Gopaluni, Philip D. Loewen, Richard D. Braatz
This paper proposes a locality preserving discriminative canonical variate analysis (LP-DCVA) scheme for fault diagnosis. The LP-DCVA method provides a set of optimal projection vectors that simultaneously maximizes the within-class mutual canonical correlations, minimizes the between-class mutual canonical correlations, and preserves the local structures present in the data. This method inherits the strength of canonical variate analysis (CVA) in handling high-dimensional data with serial correlations and the advantages of Fisher discriminant analysis (FDA) in pattern classification. Moreover, the incorporation of locality preserving projection (LPP) in this method makes it suitable for dealing with nonlinearities in the form of local manifolds in the data. The solution to the proposed approach is formulated as a generalized eigenvalue problem. The effectiveness of the proposed approach for fault classification is verified by the Tennessee Eastman process. Simulation results show that the LP-DCVA method outperforms the FDA, dynamic FDA (DFDA), CVA-FDA, and localized DFDA (L-DFDA) approaches in fault diagnosis.
New methodology for parameter estimation of offshore slug models with Hopf bifurcation Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-28 Ricardo F. Rodrigues, Jorge O. Trierweiler, Marcelo Farenzena
Offshore well elevation systems often present limit cycles caused by oil-gas slugging. The latest works in control solutions use models based on simple Ordinary Differential Equations to suppress slug flow and increase overall production. However, obtaining parameters for these models is not straightforward due to the occurrence of Hopf bifurcations. Therefore, this work aims to propose a novel methodology to improve parameter estimation. The limit cycle readings of pressure instruments are condensed into a single characteristic cycle with mean value and variance for each sample. Each iteration then uses a modified weighted sum of least squares. This creates a more convex region around the optimum that improves the performance of optimization algorithms. The results were obtained using previously published data for two models. Using the F-test, it was statistically proven that the parameters obtained from the proposed method provided an equal or better model fit by employing strictly numeric methods.
A Center-Cut Algorithm for Quickly Obtaining Feasible Solutions and Solving Convex MINLP Problems Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-27 J. Kronqvist, D.E. Bernal, A. Lundell, T. Westerlund
Here we present a center-cut algorithm for convex mixed-integer nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like several other algorithms for convex MINLP, the center-cut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order to find feasible solutions within only a few iterations. The algorithm chooses trial solutions as the center of the current linear outer approximation of the nonlinear constraints, making the trial solutions more likely to satisfy the constraints. The ability to find feasible solutions within only a few iterations makes the algorithm well suited as a primal heuristic, and we prove that the algorithm finds the optimal solution within a finite number of iterations. Numerical results show that the algorithm obtains feasible solutions quickly and is able to obtain good solutions.
Analysis of Transient Data in Test Designs for Active Fault Detection and Identification Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-28 Kyle A. Palmer, George M. Bollas
Integrated design and operation of renewables-based fuels and power production networks Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-28 Qi Zhang, Mariano Martín, Ignacio E. Grossmann
We assess the potential synergies of integrating renewables-based fuels and power production processes in one network, with a strong emphasis on the consideration of operational constraints and time-varying availability of renewable resources. We propose a multiscale mixed-integer linear programming model that combines superstructure-based synthesis and integrated production planning and scheduling. The model is applied to a particular region in Spain, where we analyze the feasibility of a renewables-based process network in terms of meeting given demands for gasoline, diesel, and electricity. The optimal and sometimes counterintuitive designs highlight the complex interactions and help identify bottlenecks in these process networks. Moreover, we solve each case using the multiscale model as well as a commonly used aggregate model; the two models obtain remarkably different solutions. The proposed multiscale model obtains high-quality solutions that stand the test of re-evaluation using a detailed model, whereas the aggregate model proposes network configurations that only satisfy small portions of the demands.
A Meta-Optimized Hybrid Global and Local Algorithm for Well Placement Optimization Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-24 Hongwei Chen, Qihong Feng, Xianmin Zhang, Sen Wang, Zhiyu Ma, Wensheng Zhou, Chen Liu
Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling Cat Swarm Optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle Swarm Optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control.
Carbon dioxide adsorption separation from dry and humid CO2/N2 mixture Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-22 Rached Ben-Mansour, Naef A.A. Qasem, Mohammed A. Antar
In this study, we report the effect of water vapor on CO2 uptake using Mg-MOF-74 via adsorption breakthrough modeling and lab experiments. Carbon dioxide is the most influencing gas that significantly expedites global warming. Therefore, it is ultimately necessary to reduce the rapid increase of CO2 concentration in the atmosphere by means of Carbon Capture and Storage (CCS). CO2 separation by physical adsorption is an interesting technology to achieve CO2 capture with minimum energy penalties. Metal-organic framework (MOF) adsorbents forms a class of adsorbents with much higher specific surface areas than conventional porous materials such as activated carbons, and zeolites. However, most MOFs show notable hydro instability for CO2 separation from humid flue gas. Mg-MOF-74 is a superior adsorbent amongst other adsorbents owing to its high CO2 uptake at flue gas conditions. A model is developed using User-Defined-Function in an ANSYS Fluent program. Two and three-dimensional models are validated by comparing their results with experimental work carried out by the authors, at ambient temperature, and published experimental data for high temperature conditions. The effect of water vapor is studied at different temperatures and various relative humidity values for Mg-MOF-74. Results indicate that CO2 uptake has been significantly reduced with the existence of more than 5% water vapor when Mg-MOF-74 is used as an adsorbent.
On the computation and physical interpretation of semi-positive reaction network invariants Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-20 Aisha Alobaid, Hossein Salami, Raymond A. Adomaitis
In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances.
Multi-objective Optimization of an Integrated Gasification Combined Cycle for Hydrogen and Electricity Production Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-15 Maan Al-Zareer, Ibrahim Dincer, Marc A. Rosen
In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an Artificial Neural Network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the neural network is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system.
Plant-Wide Oscillation Detection using Multivariate Empirical Mode Decomposition Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-15 Muhammad Faisal Aftab, Morten Hovd, Selvanathan Sivalingam
Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, its is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies.
Optimal Design of Boil-off Gas Reliquefaction Process in LNG Regasification Terminals Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-15 Harsha Nagesh Rao, Iftekhar A Karimi
Boil-off gas (BOG) generation in Liquefied Natural Gas (LNG) regasification terminals is substantial and unavoidable. Most terminals employ a cost-intensive BOG reliquefaction process using the send-out LNG. In this work, we study the preliminary design of this process with the objective of minimizing its total annualized cost (TAC). We present a comprehensive superstructure for the reliquefaction process that incorporates several process options for cooling BOG using LNG at different pressures, and allows recondensation in multiple stages. Then, we develop custom simulation/sizing modules for the process units in our superstructure, and implement a procedure to reduce explicit constraints during optimization. Considering realistic design specifications and operational constraints, we optimize a case study terminal for various BOG rates and conditions. While the TAC increases substantially with BOG rate, two-stage recondensation is always optimal. A 2-recondenser scheme with BOG cooling by the high-pressure LNG before the first recondenser is optimal for most cases.
Multiobjective decision-support tools for the choice between single-use and multi-use technologies in sterile filling of biopharmaceuticals Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-15 Haruku Shirahata, Masahiko Hirao, Hirokazu Sugiyama
In sterile filling of biopharmaceuticals, two equipment technologies are available, namely, a conventional multi-use technology using stainless steel fixed facilities, and a new single-use technology using resin-made disposable equipment. For the choice between these technologies, this study proposes a set of three multiobjective decisionsupport tools. The first tool is to evaluate cost, environmental impact, product quality, and supply robustness; the second uses a set ofweighting factors to produce a total score; the third conducts a sensitivity analysis to investigate the influence of the weighting factors on the final decision. The use of these tools was described as an activity model by a method called "the type zero method of integration definition for function modeling" (IDEF0). A case study was conducted to demonstrate the tools and the activity model in different production patterns, i.e., from small-scale and multiproduct to large-scale and single-product.
Energy efficient design of membrane processes by use of entropy production minimization Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-15 Elisa Magnanelli, Øivind Wilhelmsen, Eivind Johannessen, Signe Kjelstrup
To minimize entropy production means to reduce the lost work in a process, and to optimize the use of energy resources. Due to the need for re-compression, membrane units for separation of CO2 from natural gas require large amounts of electrical power. We show that this power requirement can be reduced by controlling the permeation process so that the entropy production is minimum. With the use of optimal control theory, we develop in this work a detailed and robust method to minimize the entropy production of a membrane unit for separation of CO2 from natural gas, by control of the partial and total pressures on the permeate side. Moreover, we show how the continuous optimal results can serve as ideal limits for the practical design. A three-step permeate pressure that approximates the optimum reduces both the entropy production and the compressor power, when the permeate gas is re-compressed.
Performance of an active disturbance rejection control on a simulated continuous microalgae photobioreactor Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-14 Claudia Lorena Garzón-Castro, Efredy Delgado-Aguilera, John Alexander Cortés-Romero, Edisson Tello, Gianfranco Mazzanti
Microalgae are used for the industrial production of high value compounds. The aim in continuous bioreactors is to obtain the highest biomass production. It is necessary to guarantee that the bioprocesses attain and maintain the optimal reference biomass CX*(t), despite endogenous and exogenous disturbances. This paper describes the numerical simulation of the application of Active Disturbance Rejection Control (ADRC) to control the dilution rate (D(t)) in a continuous culture of the microalga Chlorella vulgaris. To reduce the bioprocess to a “SISO” system, the authors chose the dilution rate, D(t), to be the only control signal. The control proposal was illustrated and evaluated through a numerical simulation using MATLAB/Simulink™ environment. The performance of the ADRC was tested by the application of external perturbations and variation of parameters over a nominal case. At nominal conditions, D(t) was always maintained within the physical limits imposed by the bioprocess. Step and smooth type signals, at 96.4%•|Dmax(t)|, were imposed as external perturbation on the control signal input, D(t). The controller response kept the output signal CX(t) within an insignificant 0.0043%•|CXmax(t)|. The algal culture had a strongly asymmetric response to variations of the ideal maximum growth rate, μmax(t) ± 30%•|μmax(t)|, and of the nominal light intensity, Iin(t) ± 30%•|Iin(t)|. Nonetheless, the controller promptly returned the output signal to its reference value, CX(t)*. The numerical test of the control proposal, in summary, showed that the ADRC strategy ensures excellent reference tracking capability and robustness towards parametric uncertainties, un-modeled dynamics, and external disturbances.
Explicit formulas for reaction probability in reaction-diffusion experiments Comput. Chem. Eng. (IF 3.113) Pub Date : 2016-06-23 M. Wallace, R. Feres, G. Yablonsky, A. Stern
A computational procedure is developed for determining the conversion probability for reaction-diffusion systems in which a first-order catalytic reaction is performed over active particles. We apply this general method to systems on metric graphs, which may be viewed as 1-dimensional approximations of 3-dimensional systems, and obtain explicit formulas for conversion. We then study numerically a class of 3-dimensional systems and test how accurately they are described by model formulas obtained for metric graphs. The optimal arrangement of active particles in a 1-dimensional multiparticle system is found, which is shown to depend on the level of catalytic activity: conversion is maximized for low catalytic activity when all particles are bunched together close to the point of gas injection, and for high catalytic activity when the particles are evenly spaced.
The switching point between kinetic and thermodynamic control Comput. Chem. Eng. (IF 3.113) Pub Date : 2016-07-01 P. Daniel Branco, Gregory Yablonsky, Guy B. Marin, Denis Constales
In organic chemistry, the switching point between the kinetic and thermodynamic control regimes of two competitive, parallel reactions is widely studied. A new definition for this switching point is proposed: the time at which the rates of formation of the competing products are equal. According to this definition, the kinetic control regime is present from the beginning of the reaction, and is valid as long as the rate of formation of the kinetic product is larger than the rate of formation of the thermodynamic product. On the switching point, both rates of formation are equal, so, from this switching point the thermodynamic product has a larger rate of formation, and the thermodynamic control remains until the end of the reaction. A closed form expression is given for the proposed time of the switching point, as a function of the direct and inverse kinetic constants of both competing reactions, as well as the initial concentrations of the starting reagent and the competing products. The concept of competing control regimes is extended also to the case where the reactions start from two competitive reagents which decompose to produce a single product.
☆A novel method to compute the time dependence of state distributions in the stochastic kinetic description of an autocatalytic system Comput. Chem. Eng. (IF 3.113) Pub Date : 2016-08-03 Gábor Lente
A novel method is introduced to estimate the solution of the stochastic kinetic master equation of an autocatalytic reaction scheme. The method first computes an accurate probability master distribution for relatively low particle numbers, then uses a time shift property of the distributions to extend this solution to the entire state space and time scale. A limited comparison with accurately calculated probability distributions suggests that the approximation works exceptionally well and is able to provide a very reliable way to obtain the solution of the stochastic master equation of a system without any limitation on its size. The method will most probably be applicable with minor modifications for the stochastic kinetic description of other irreversible reactions as well.
Numerical methods and Fischer-Tropsch complex reaction network generation for steady state isotopic transient kinetic analysis Comput. Chem. Eng. (IF 3.113) Pub Date : 2016-08-05 Jonas Van Belleghem, Denis Constales, Joris W. Thybaut, Guy B. Marin
A versatile modeling strategy for Steady State Isotopic Transient Kinetic Analysis (SSITKA) data, acquired in a plug flow reactor, is elaborated with particular attention to complex reaction networks and Fischer-Tropsch Synthesis as example. A spatial discretization scheme optimizing accuracy and CPU time is developed. For low switch time constants, the van Leer and van Albada flux limiter functions used in conjunction with the DASPK solver yields the lowest CPU time for the integration of the resulting ordinary differential equations. For larger switch time constants, conventional central differencing can be applied. A network generation methodology is implemented accounting for the isotopic labeling. It reduces the exponential dependence of the number of considered species on the carbon number to a quadratic dependence. For a reaction network allowing a maximum chain length of 5 carbon atoms a gain in CPU time up to a factor of 10 can be achieved.
Elucidating and handling effects of valve-induced nonlinearities in industrial feedback control loops Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-08-24 Helen Durand, Robert Parker, Anas Alanqar, Panagiotis D. Christofides
In this work, we investigate the effects of various types of valve behavior (e.g., linear valve dynamics and stiction) on the effectiveness of process control in a unified framework based on systems of nonlinear ordinary differential equations that characterize the dynamics of closed-loop systems including the process, valve, and controller dynamics. By analyzing the resulting dynamic models, we demonstrate that the responses of the valve output and process states when valve behavior cannot be neglected (e.g., stiction-induced oscillations in measured process outputs) are closed-loop effects that can be difficult to predict a priori due to the coupled and typically nonlinear dynamics of the process-valve model. Subsequently, we discuss the implications of this closed-loop perspective on the effects of valve dynamics in closed-loop systems for understanding valve behavior compensation techniques and developing new ones. We conclude that model-based feedback control designs that can account for process and valve constraints and dynamics provide a systematic method for handling the multivariable interactions in a process-valve system, where the models in such control designs can come either from first-principles or empirical modeling techniques. The analysis also demonstrates the necessity of accounting for valve behavior when designing a control system due to the potentially different consequences under various control methodologies of having different types of valve behavior in the loop. Throughout the work, a level control example and a continuous stirred tank reactor example are used to illustrate the developments.
Generalized robust counterparts for constraints with bounded and unbounded uncertain parameters Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-09-19 Logan R. Matthews, Yannis A. Guzman, Christodoulos A. Floudas
Robust optimization has emerged as a powerful and efficient methodology for incorporating uncertain parameters into optimization models. In robust optimization, robust counterparts for uncertain constraints are created by imposing a known set of uncertain parameter realizations onto the new robust constraint. For constraints with all bounded parameters, the interval + ellipsoidal and interval + polyhedral uncertainty sets are well-established in robust optimization literature, while box, ellipsoidal, or polyhedral sets may be used for unbounded parameters. However, there has yet to be any counterparts proposed for constraints that simultaneously contain both bounded and unbounded parameters. This is crucial, as using the traditional box, ellipsoidal, or polyhedral sets with bounded parameters may impose impossible parameter realizations outside of their bounds, unnecessarily increasing the conservatism of results. In this work, robust counterparts for uncertain constraints with both bounded and unbounded uncertain parameters are derived: the generalized interval + box, generalized interval + ellipsoidal, and generalized interval + polyhedral counterparts. These counterparts reduce to the traditional box, ellipsoidal, and polyhedral counterparts if all parameters are unbounded, and reduce to the traditional interval + ellipsoidal and interval + polyhedral counterparts if all parameters are bounded. It is proven that established a priori probabilistic bounds remain valid for these counterparts. The importance of these developments is demonstrated with computational examples, showing the reduction of conservatism that is gained by appropriately limiting the possible realizations of the bounded parameters. The developments increase the scope and applicability of robust optimization as a tool for optimization under uncertainty.
Parallel cyclic reduction decomposition for dynamic optimization problems Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-05 Wei Wan, John P. Eason, Bethany Nicholson, Lorenz T. Biegler
Direct transcription of dynamic optimization problems, with differential-algebraic equations discretized and written as algebraic constraints, can create very large nonlinear optimization problems. When this discretized optimization problem is solved with an NLP solver, such as IPOPT, the dominant computational cost often lies in solving the linear system that generates Newton steps for the KKT system. Computational cost and memory constraints for this linear system solution raise many challenges as the system size increases. On the other hand, the linear KKT system for our dynamic optimization problem is sparse and structured, and can be permuted to form a block tridiagonal matrix. This study explores a parallel decomposition strategy for block tridiagonal systems that is based on cyclic reduction (CR) factorization of the KKT matrix. The classical CR method has good observed performance, but its numerical stability properties need further study for our KKT system. Finally, we discuss modifications to the CR decomposition that improve performance, and we apply the approach to four industrially relevant case studies. On the largest problem, a parallel speedup of a factor of four is observed when using eight processors.
An optimization based strategy for crude selection in a refinery with lube hydro-processing Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-08 Kyungseok Noh, Joohyun Shin, Jay H. Lee
An optimization-based strategy is developed for crude selection in a refinery with lube base oil (LBO) producing capability. Crude oil is classified into five types based on the viscosity index (VI) and the compositional aspect of vacuum gas oil (VGO). A prediction model for the VI, the most important quality variable in LBO processing, is developed for VGO based on its bulk properties. For each crude type, prediction models for the yield and the VI change vs. the conversion rate during lube hydro-processing are developed. The lube hydro-processing models are then incorporated into the overall refinery optimization to maximize the overall margin while satisfying all the specifications of the lube and fuel products simultaneously. A case study involving several price scenarios illustrates the benefits from using the model-based optimization method for deciding crude procurement, the grade of the LBO to be produced, and the conversion rate in the lube process.
A stoichiometric method for reducing simulation cost of chemical kinetic models Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-06 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.
Robust Dynamic Optimization of Batch Processes under Parametric Uncertainty: Utilizing Approaches from Semi-infinite Programs Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-05 Jennifer Puschke, Hatim Djelassi, Johanna Kleinekorte, Ralf Hannemann-Tamás, Alexander Mitsos
Software approach to simulation-based hazard identification of complex industrial processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2018-06-05 Ján Janošovský, Matej Danko, Juraj Labovský, Ľudovít Jelemenský
Process safety is of major importance in chemical industry. Numerous activities have targeted the modification of conventional risk assessment strategies by computer aided approach. In this paper, a software tool for Hazard and Operability (HAZOP) study based on process simulation is presented. Individual components of the proposed software tool are described and the principal methodology of their function is explained. As the simulation engine, commercial process simulator Aspen HYSYS was employed. Proposed tool was applied to a case study of an ammonia synthesis plant based on an existing plant. Hazardous events and operability problems in the syngas purification unit and ammonia synthesis loop have been detected and reported. The steady state multiplicity phenomenon in the ammonia synthesis loop has also been successfully identified. Based on simulation data evaluation performed in the semi-automatic manner by the proposed tool, a HAZOP-like report containing HAZOP deviations and their causes and consequences was generated.
Framework for a smart data analytics platform towards process monitoring and alarm management Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-12 Wenkai Hu, Sirish L. Shah, Tongwen Chen
The fusion of information from disparate sources of data is the key step in devising strategies for a smart analytics platform. In the context of the application of analytics in the process industry, this paper provides a framework for seamless integration of information from process and alarm databases complimented with process connectivity information. The discovery of information from such diverse data sources can be subsequently used for process and performance monitoring including alarm rationalization, root cause diagnosis of process faults, hazard and operability analysis, safe and optimal process operation. The utility of the proposed framework is illustrated by several successful industrial case studies.
Machine learning: Overview of the recent progresses and implications for the process systems engineering field Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-13 Jay H. Lee, Joohyun Shin, Matthew J. Realff
Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.
Petroleum production optimization – A static or dynamic problem? Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-16 Bjarne Foss, Brage Rugstad Knudsen, Bjarne Grimstad
This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance maximizing the revenue stream. This is done by adjusting control inputs like choke valves, artificial lift parameters and routing of well streams. It is well known that the daily production optimization problem is well suited for mathematical optimization. The contribution of this paper is a discussion on appropriate formulations, in particular the use of static models vs. dynamic models. We argue that many important problems can indeed be solved by repetitive use of static models while some problems, in particular related to shale gas systems, require dynamic models to capture key process characteristics. The reason for this is how reservoir dynamics interacts with the dynamics of the production system.
Scheduling, optimization and control of power for industrial cogeneration plants Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-17 Rahul Bindlish
Scheduling, optimization and control of power for three industrial cogeneration plants at one of Dow’s Louisiana site is presented in this paper. A first principle mathematical model that includes mass and energy balances for gas turbines, heat recovery units, steam turbines, pressure relief valves and steam headers is used to formulate multiple optimization problems to recommend the best strategy to trade power. The model has detailed operational information that includes equipment status and control curves for different operating scenarios. The scheduled power offer curve is obtained by solving multiple optimization problems using the validated process model along with operational and equipment limitations. Adjustment of power schedule offer is done in the real-time market thirty minutes prior to the hour and implementation of the dispatched power schedule is done using a model predictive controller.
Decomposing complex plants for distributed control: Perspectives from network theory Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-19 Prodromos Daoutidis, Wentao Tang, Sujit S. Jogwar
This paper reviews recent research on the application of methods from the theory of networks for developing distributed control architectures for complex plants. The problem is defined as one of decomposing process networks into constituent subnetworks with strong intra-subnetwork and weak inter-subnetwork interactions. These interactions are quantified based on connectivity and response sensitivity information. This perspective is inspired by the community detection problem in networks. Several approaches are discussed based on hierarchical clustering and modularity optimization. The concepts and potential of these methods for developing control architectures for complex plants are illustrated through a case study. Future research directions are also discussed.
An algorithm for gradient-based dynamic optimization of UV flash processes Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-19 Tobias K.S. Ritschel, Andrea Capolei, Jozsef Gaspar, John Bagterp Jørgensen
This paper presents a novel single-shooting algorithm for gradient-based solution of optimal control problems with vapor–liquid equilibrium constraints. Such optimal control problems are important in several engineering applications, for instance in control of distillation columns, in certain two-phase flow problems, and in operation of oil reservoirs. The single-shooting algorithm uses an adjoint method for the computation of gradients. Furthermore, the algorithm uses either a simultaneous or a nested approach for the numerical solution of the dynamic vapor–liquid equilibrium model equations. Two numerical examples illustrate that the simultaneous approach is faster than the nested approach and that the efficiency of the underlying thermodynamic computations is important for the overall performance of the single-shooting algorithm. We compare the performance of different optimization software as well as the performance of different compilers in a Linux operating system. These tests indicate that real-time nonlinear model predictive control of UV flash processes is computationally feasible.
Advanced optimization strategies for integrated dynamic process operations Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-20 Lorenz T. Biegler
Modern approaches for dynamic optimization trace their inception to Pontryagin's Maximum Principle 60 years ago. Since then the application of large-scale nonlinear programming strategies has been extended to deal with challenging real-world process optimization problems. This study discusses and demonstrates the effectiveness of dynamic optimization on three case studies on real-world chemical processes. In the first case, we consider the optimal design of runaway reactors, where simulation models may lead to unbounded profiles for many choices of design and operating conditions. As a result, optimization based on repeated simulations typically fails, and a simultaneous, equation-based approach must be applied. Second, we consider optimal operating policies for grade transitions in polymer processes. Modeled as an optimal control problem, we demonstrate how incorporation of product specification bands leads to multi-stage formulations that greatly improve process performance and significantly reduce off-grade product. Third, we consider an optimization strategy for the integration of scheduling and dynamic process operation for general continuous/batch processes. The method introduces a discrete time formulation for simultaneous optimization of scheduling and operating decisions. Finally, we provide a concise summary of directions and challenges for future extension of these optimization formulations and solution strategies.
On improving the online performance of production scheduling: Application to air separation units Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-10-27 Irene Lotero, Ajit Gopalakrishnan, Thierry Roba
In the operation of power-intensive Air Separation Units (ASUs) that produce storable liquid products, optimization opportunities exist at two time scales − week-ahead production scheduling to leverage fluctuations in electricity prices, and real-time decisions that optimize the entire plant operation and capture spot opportunities. In our previous work, we proposed a methodology based on flexibility analysis and robust optimization to ensure feasibility of real-time operational decisions at ASUs for future time periods within a scheduling horizon. In this paper, we build upon the methodology to propose approaches to improve the online performance of a production schedule for ASUs by using the real-time optimization layer. We compare several policies for real-time optimization and our studies on real plant data show interesting trade-offs between week-ahead scheduling and real-time optimization.
A multitree approach for global solution of ACOPF problems using piecewise outer approximations Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-11-01 Jianfeng Liu, Michael Bynum, Anya Castillo, Jean-Paul Watson, Carl D. Laird
Electricity markets rely on the rapid solution of the optimal power flow (OPF) problem to determine generator power levels and set nodal prices. Traditionally, the OPF problem has been formulated using linearized, approximate models, ignoring nonlinear alternating current (AC) physics. These approaches do not guarantee global optimality or even feasibility in the real ACOPF problem. We introduce an outer-approximation approach to solve the ACOPF problem to global optimality based on alternating solution of upper- and lower-bounding problems. The lower-bounding problem is a piecewise relaxation based on strong second-order cone relaxations of the ACOPF, and these piecewise relaxations are selectively refined at each major iteration through increased variable domain partitioning. Our approach is able to efficiently solve all but one of the test cases considered to an optimality gap below 0.1%. Furthermore, this approach opens the door for global solution of MINLP problems with AC power flow equations.
The impact of digitalization on the future of control and operations Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-11-01 Alf J. Isaksson, Iiro Harjunkoski, Guido Sand
The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated. Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT.
Dynamic latent variable analytics for process operations and control Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-11-04 Yining Dong, S. Joe Qin
After introducing process data analytics using latent variable methods and machine learning, this paper briefly review the essence and objectives of latent variable methods to distill desirable components from a set of measured variables. These latent variable methods are then extended to modeling high dimensional time series data to extract the most dynamic latent time series, of which the current values are best predicted from the past values of the extracted latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods. The extracted features reveal hidden information in the data that is valuable for understanding process variability.
Analysis of the multiplicity of steady-state profiles of two tubular reactor models Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-11-04 D. Dochain
This paper deals with the analysis of two tubular reactor models, the non-isothermal tubular reactor model and a biochemical reactor model. It is mathematically shown in particular that multiple equilibrium profiles can be exhibited if the diffusion coefficients are large enough by considering regular perturbation arguments.
Municipal solid waste to liquid transportation fuels – Part III: An optimization-based nationwide supply chain management framework Comput. Chem. Eng. (IF 3.113) Pub Date : 2017-11-05 Alexander M. Niziolek, Onur Onel, Yuhe Tian, Christodoulos A. Floudas, Efstratios N. Pistikopoulos
An optimization-based supply chain management framework for municipal solid waste (MSW) to liquid transportation fuels (WTL) processes is presented. First, a thorough analysis of landfill operations and annual amounts of MSW that are deposited across the contiguous United States is conducted and compared with similar studies. A quantitative supply chain framework that simultaneously accounts for the upstream and downstream WTL value chain operations is then presented. A large-scale mixed-integer linear optimization model that captures the interactions among MSW feedstock availabilities and locations, WTL refinery locations, and product delivery locations and demand capacities is described. The model is solved for both the nationwide and statewide WTL supply chains across numerous case studies. The results of the framework yield insights into the strategic placement of WTL refineries in the United States as well as topological information on the feedstock and product flows. The results suggest that large-scale WTL supply chains can be competitive, with breakeven oil prices ranging between $64–$77 per barrel.
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