Journal Sciences News
Veterinary Clinics of North America: Equine Practice
4 March 2018
Heat exchanger network cleaning scheduling: From optimal control to mixed-Integer decision making
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Riham Al Ismaili, Min Woo Lee, D. Ian Wilson, Vassilios S. Vassiliadis An approach for optimising the cleaning schedule in heat exchanger networks (HENs) subject to fouling is presented. This work focuses on HEN applications in crude oil preheat trains located in refineries. Previous approaches have focused on using mixed-integer nonlinear programming (MINLP) methods involving binary decision variables describing when and which unit to clean in a multi-period formulation. This work is based on the discovery that the HEN cleaning scheduling problem is in actuality a multistage optimal control problem (OCP), and further that cleaning actions are the controls which appear linearly in the system equations. The key feature is that these problems exhibit bang-bang behaviour, obviating the need for combinatorial optimisation methods. Several case studies are considered; ranging from a single unit up to 25 units. Results show that the feasible path approach adopted is stable and efficient in comparison to classical methods which sometimes suffer from failure in convergence.
4 March 2018
Rigorous design of reaction-separation processes using disjunctive programming models
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Xiang Zhang, Zhen Song, Teng Zhou A systematic and efficient method for the rigorous design of complex chemical processes is significant in the chemical industry. In this paper, a superstructure-based optimization approach for the rigorous and simultaneous design of reaction and separation processes using generalized disjunctive programming (GDP) models is presented. In the reactor network, disjunctions for conditional reactors are introduced where the balance and reaction kinetic equations are applied only if the reactor is selected. Based on the proposed reactor disjunctions, two different reactor superstructures are developed and employed. In addition, the GDP representation of distillation columns is used to model the separation network. The reliability and efficiency of the proposed optimization method are demonstrated on two case studies, i.e., one cyclohexane oxidation process and one benzene chlorination process. The flowsheet structure and process-unit operating conditions are simultaneously optimized to minimize the total annual cost of the processes.
4 March 2018
A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Zhiping Zhang, Dingjiang Chen, Yuzhong Feng, Zhihong Yuan, Bingzhen Chen, Weizhong Qin, Shengwu Zou, Shui Qin, Jifei Han Operational agility, which represents the capability of the plant/facility regarding the fast detection and adaption to the new situations facing external/internal changes, is commonly regarded as one of central-properties for Smart Process Manufacturing. Clearly, operational agility significantly affects the plant/facility performance such as profit and safety. In this work, a strategy for enhancing the operational agility of petroleum refinery plants is proposed. For this strategy, the accumulated data sets from the industrial plants as well as the high-fidelity simulation activities are firstly processed to formulate the case base with a determined structure. Fuzzy matching is adopted to evaluate the similarity between the new coming case and the potential one in the formulated case base. A new criterion, namely stability number, is proposed as the performance metric for choosing an appropriate type of Fuzzy membership function (FMF). Furthermore, an optimization model is set to optimize parameters of the selected Fuzzy membership function. The application of the proposed strategy to an industrial Fluidized Catalytic Cracking Unit (FCCU) is performed to demonstrate the relevant effectiveness.
4 March 2018
Chemical process systems analysis using thermodynamic balance equations with entropy generation. Revaluation and extension
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): John P. O'Connell Modeling inconsistencies exhibited in previous work (O'Connell, 2017) associated with post-combustion methanolamine (MEA) and ammonia (NH3) absorption processes have been revealed. The origin of the problem was that entropies for ionic reactions were not evaluated from input model equilibrium constants regressed from data. Revised calculations have been made using only properties of formation. Positive and consistent entropy generation rates ( S
4 March 2018
Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Wentao Tang, Andrew Allman, Davood Babaei Pourkargar, Prodromos Daoutidis Distributed optimization, based on a decomposition of the entire optimization problem, has been applied to many complex decision making problems in process systems engineering, including nonlinear model predictive control. While decomposition techniques have been widely adopted, it remains an open problem how to optimally decompose an optimization problem into a distributed structure. In this work, we propose to use community detection in network representations of optimization problems as a systematic method of partitioning the optimization variables into groups, such that the variables in the same groups generally share more constraints than variables between different groups. The proposed method is applied to the decomposition of the optimal control problem involved in the nonlinear model predictive control of a reactor-separator process, and the quality of the resulting decomposition is examined by the resulting control performance and computational time. Our result suggests that community detection in network representations of the optimization problem generates decompositions with improvements in computational performance as well as a good optimality of the solution.
4 March 2018
Integrated scheduling of rolling sector in steel production with consideration of energy consumption under time-of-use electricity prices
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Shengnan Zhao, Ignacio E. Grossmann, Lixin Tang Due to increasing load and penetration of renewables, the electric grid is using time-of-use pricing for industrial customers. Involving energy-intensive processes, steel companies can reduce their production cost by accounting for changes in electricity pricing. In particular, steel companies can take advantage of processing flexibility to make better use of electric power, and thus reduce the energy cost. In this paper, we address a new integrated scheduling problem of multi-stage production derived from the rolling sector of steel production, with consideration of campaign decisions and demand-side management. The problem is formulated as a continuous time mixed-integer nonlinear programming (MINLP) model with generalized disjunctive programming (GDP) constraints, which is then reformulated as a mixed-integer linear programming (MILP) model. Numerical results are presented to demonstrate that the model is computationally efficient and compact.
4 March 2018
A systematic approach for modeling of waterflooding process in the presence of geological uncertainties in oil reservoirs
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Farzad Hourfar, Karim Salahshoor, Hosein Zanbouri, Ali Elkamel, Peyman Pourafshary, Behzad Moshiri In this paper, a systematic approach which is able to consider different types of geological uncertainty is presented to model the waterflooding process. The proposed scheme, which is based on control and system theories, enables the experts to apply suitable techniques to optimize the production. By using the developed methodology, a reasonable mapping between defined system inputs and outputs in frequency domain and around a specific operating point is established. In addition, a nominal model for the process as well as a lumped representation for uncertainty effects are provided. Based on the proposed modeling mechanism, reservoir management goals can be pursued in the presence of uncertainty by utilization of complicated control and optimization strategies. The developed algorithm has been simulated on 10th SPE-model#2. Observed results have shown that the introduced methodology is able to effectively model the dynamics of waterflooding process, while taking into account the assumed induced geological uncertainty.
4 March 2018
State estimation of wastewater treatment plants based on model approximation
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Xunyuan Yin, Jinfeng Liu In this article, we consider state estimation of wastewater treatment plants based on model approximation. In particular, we consider a wastewater treatment plant described by the Benchmark Simulation Model No.1 which consists of a five-chamber reactor and a settler. We propose to use the proper orthogonal decomposition approach with re-identification of output equations to obtain a reduced-order model of the original system. Then, the reduced-order model is taken advantage of in state estimation. An approach on how to determine an appropriate minimum measurement set is also proposed based on degree of observability. A continuous-discrete extended Kalman filtering algorithm is used to design the estimator based on the reduced-order model. We show through extensive simulations under different weather conditions that the estimator based on the reduced-order model with re-identified output equations gives good state estimates of the actual process.
4 March 2018
Bounded-error optimal experimental design via global solution of constrained min–max program
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Olga Walz, Hatim Djelassi, Adrian Caspari, Alexander Mitsos We present an improvement of existing methods for globally solving optimal experimental design (OED) for bounded-error estimation based on a bilevel formulation from Mukkala et al. (2017). The proposed solution method for the min–max program is based on our method for generalized semi-infinite programs (via restriction of the right-hand side). The algorithm employed has the advantage that it guarantees a global solution for the OED assuming the global solution of two subproblems. To obtain a feasible solution only the lower-level problem has to be solved globally. In case of a local solution of the upper-level problem, the solution is still feasible though it is an upper bound of the global solution. The min–max method for OED is illustrated with four examples: two simple chemical reactions, BET-adsorption and a reformulated predator-prey system. The benefits of global methods are shown along with the limitations of state-of-the-art global solvers.

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2 February 2018
Process modelling, simulation and technoeconomic evaluation of crystallisation antisolvents for the continuous pharmaceutical manufacturing of rufinamide
Publication date: 4 March 2018
Source:Computers & Chemical Engineering, Volume 111 Author(s): Samir Diab, Dimitrios I. Gerogiorgis Continuous Pharmaceutical Manufacturing (CPM) is a promising new paradigm to produce active pharmaceutical ingredients (APIs), allowing reduced equipment dimensions, lower waste production and energy consumption, and safer operation in comparison to the industrially dominant batch methods. Rufinamide is an antiepileptic agent whose demonstrated continuous flow synthesis (featuring three reactions in flow) circumvents the accumulation of toxic and explosive organoazide intermediates. To ascertain the feasibility and viability of this continuous synthetic route, systematic process modelling and costing is required. This paper presents a technoeconomic analysis of the upstream continuous flow synthesis of rufinamide via steady-state process modelling and plantwide simulation. Reaction kinetics and Arrhenius parameters are estimated from previously published experimental data, and plug flow reactor (PFR) volumes are calculated towards rigorous plant costing. Continuous reactor and separator units have been designed, and the CPM flowsheet is compared vs. the batch production method, with respect to technical efficiency and profitability. Plantwide costing via an established economic analysis methodology has been pursued to enable a detailed comparison of cost items towards process scale-up, as well as motivate the need for further systematic optimisation.

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2 February 2018
APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Li Ang Zhang, Alisa Urbano, Gilles Clermont, David Swigon, Ipsita Banerjee, Robert S. Parker The inverse problem associated with fitting parameters of an ordinary differential equation (ODE) system to data is nonlinear and multimodal, which is of great challenge to gradient-based optimizers. Markov Chain Monte Carlo (MCMC) techniques provide an alternative approach to solving these problems and can escape local minima by design. APT-MCMC was created to allow users to setup ODE simulations in Python and run as compiled C++ code. It combines affine-invariant ensemble of samplers and parallel tempering MCMC techniques to improve the simulation efficiency. Simulations use Bayesian inference to provide probability distributions of parameters, which enable analysis of multiple minima and parameter correlation. Benchmark tests result in a 20
2 February 2018
A novel robust optimization approach for an integrated municipal water distribution system design under uncertainty: A case study of Mashhad
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Zabih Ghelichi, Javad Tajik, Mir Saman Pishvaee This paper proposes a novel robust optimization (RO) approach along with a two-stage scenario-based stochastic programming to optimize a municipal water distribution system (WDS) under demand and rainfall uncertainties. Firstly, we have proposed a new multi-period mixed-integer linear programming (MILP) formulation of a municipal WDS. The goal is to find solutions that are both cost-effective and completely fulfill potable and non-potable demand in an integrated system. Furthermore, a novel RO approach is developed which attempts to adjust protection level in a column what we call “adjustable column-wise robust optimization”. The interesting point of the proposed RO approach is its linear structure and being computationally tractable. The efficiency of the proposed models are evaluated through a real case study of Mashhad. The acquired results reveal the proposed WDS model have dramatically reduced the total costs. Simultaneously, the RO approach has risen robustness besides realization demonstrates its better performance than deterministic one.
2 February 2018
Distributionally robust optimization for planning and scheduling under uncertainty
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Chao Shang, Fengqi You Distributionally robust optimization (DRO) is an emerging and effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty. We propose an effective DRO framework for planning and scheduling under demand uncertainties. A novel data-driven approach is proposed to construct ambiguity sets based on principal component analysis and first-order deviation functions, which help excavating accurate and useful information from uncertainty data. Moreover, it leads to mixed-integer linear reformulations of planning and scheduling problems. To account for the multi-stage sequential decision-making structure in process operations, we further develop multi-stage DRO models and adopt affine decision rules to address the computational issue. Applications in industrial-scale process network planning and batch process scheduling demonstrate that, the proposed DRO approach can effectively leverage uncertainty data information, better hedge against distributional ambiguity, and yield more profits.
2 February 2018
An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Morgan T. Kelley, Richard C. Pattison, Ross Baldick, Michael Baldea The emphasis currently placed on enterprise-wide decision making and optimization has led to an increased need for methods of integrating nonlinear process dynamics and control information in scheduling calculations. The inevitable high dimensionality and nonlinearity of first-principles dynamic process models makes incorporating them in scheduling calculations challenging. In this work, we describe a general framework for deriving data-driven surrogate models of the closed-loop process dynamics. Focusing on Hammerstein–Wiener and finite step response (FSR) model forms, we show that these models can be (exactly) linearized and embedded in production scheduling calculations. The resulting scheduling problems are mixed-integer linear programs with a special structure, which we exploit in a novel and efficient solution strategy. A polymerization reactor case study is utilized to demonstrate the merits of this method. Our framework compares favorably to existing approaches that embed dynamics in scheduling calculations, showing considerable reductions in computational effort.
2 February 2018
Global optimisation of multi-plant manganese alloy production
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Martin Naterstad Digernes, Lars Rudi, Henrik Andersson, Magnus St
2 February 2018
Efficient simulation of chromatographic separation processes
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Solomon F. Brown, Mark D. Ogden, Eric S. Fraga This work presents the development and testing of an efficient, high resolution algorithm developed for the solution of equilibrium and non-equilibrium chromatographic problems as a means of simultaneously producing high fidelity predictions with a minimal increase in computational cost. The method involves the coupling of a high-order WENO scheme, adapted for use on non-uniform grids, with a piecewise adaptive grid (PAG) method to reduce runtime while accurately resolving the sharp gradients observed in the processes under investigation. Application of the method to a series of benchmark chromatographic test cases, within which an increasing number of components are included over short and long spatial domains and containing shocks, shows that the method is able to accurately resolve the discontinuities and that the use of the PAG method results in a reduction in the CPU runtime of up to 90%, without degradation of the solution, relative to an equivalent uniform grid.
2 February 2018
A cost-effective retrofit of conventional distillation sequence to dividing-wall prefractionator configuration
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Le Quang Minh, Tram Ngoc Pham, Nguyen Van Duc Long, Joonho Shin, Moonyong Lee A dividing-wall prefractionator configuration was investigated for a safe and economic retrofit of the conventional sequence using simple distillation columns. In the proposed retrofit configuration, the first column was modified to a dividing wall column as a prefractionator to supply prefractionated multi-feeds to the subsequent column. To investigate the effectiveness of the proposed configuration, nine near-ideal feed mixtures were considered for analysis. The proposed configuration was then compared with several other alternative configurations. The proposed dividing-wall prefractionator efficiently generates prefractionated multi-feed streams avoiding feed mismatch and remixing effect with low modification cost. Moreover, because the proposed retrofit configuration allows for flexible switching between the dividing-wall prefractionator and the conventional operating mode, a safe retrofit is also ensured by reducing the operational risks. Several industrial retrofit cases were studied to validate the proposed dividing-wall prefractionator configuration.

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2 February 2018
Challenges and opportunities in biopharmaceutical manufacturing control
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Moo Sun Hong, Kristen A. Severson, Mo Jiang, Amos E. Lu, J. Christopher Love, Richard D. Braatz This article provides a perspective on control and operations for biopharmaceutical manufacturing. Challenges and opportunities are described for (1) microscale technologies for high-speed continuous processing, (2) plug-and-play modular unit operations with integrated monitoring and control systems, (3) dynamic modeling of unit operations and entire biopharmaceutical manufacturing plants to support process development and plant-wide control, and (4) model-based control technologies for optimizing startup, changeover, and shutdown. A challenge is the ability to simultaneously address the uncertainties, nonlinearities, time delays, non-minimum phase behavior, constraints, spatial distributions, and mixed continuous-discrete operations that arise in biopharmaceutical operations. The design of adaptive and hybrid control strategies is discussed. Process data analytics and grey-box modeling methods are needed to deal with the heterogeneity and tensorial dimensionality of biopharmaceutical data. Novel bioseparations as discussed as a potential cost-effective unit operation, with a discussion of challenges for the widespread application of crystallization to therapeutic proteins.
2 February 2018
A multifluid-PBE model for simulation of mass transfer limited processes operated in bubble columns
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): Camilla Berge Vik, Jannike Solsvik, Magne Hillestad, Hugo A. Jakobsen Modeling of reactive dispersed flows with interfacial mass transfer limitations require an accurate description of the interfacial area, mass transfer coefficient and the driving force. The driving force is given by the difference in species composition between the continuous and dispersed phases and thus depends on bubble size. This paper shows the extension of the multifluid-PBE model to reactive and non-isothermal flows with novel transport equations for species mass and temperature which are continuous functions of bubble size. The model is demonstrated by simulating the Fischer-Tropsch synthesis operated in a slurry bubble column at industrial conditions. The simulation results show different composition and velocity for the smallest and largest bubbles. The temperature profile was independent of bubble size due to efficient heat exchange. The proposed model is particularly useful in investigating the effects of bubble size on strongly mass transfer limited processes operated in the heterogeneous flow regime.
Available online 17 January 2018
Robust integrated production-maintenance scheduling for an evaporation network
Publication date: 2 February 2018
Source:Computers & Chemical Engineering, Volume 110 Author(s): C.G. Palac
Available online 13 January 2018
Optimal Operation of Parallel Distillation Systems with Multiple Product Grades: An Industrial Case Study
Publication date: Available online 17 January 2018
Source:Computers & Chemical Engineering Author(s): Yingyan Luo, Qi Zhang, Lingyu Zhu, Xi Chen In the fine chemical industry, customers often demand different grades with different purity specifications. To achieve the best performance, the production tasks should be assigned to different distillation columns at the most suitable operating conditions and time periods. In this paper, an optimal scheduling method is presented through an industrial case study with multiple products and parallel distillation columns. Rigorous nonlinear models are built for each distillation column and validated with plant data, based on which, a reduced-order model is obtained with data of optimal operating points at various conditions. The reduced-order model is then incorporated into a mode-based discrete-time mixed integer linear program (MILP) scheduling model, where transitions between different operating modes are specified based on plant data. The MILP-based scheduling is applied to a real-word industrial case study to demonstrate its computational efficiency and effectiveness in improving economic performance with comparison to two heuristic scheduling methods.
Available online 11 January 2018
Robust Optimization for Decision-making under Endogenous Uncertainty
Publication date: Available online 13 January 2018
Source:Computers & Chemical Engineering Author(s): Nikolaos H. Lappas, Chrysanthos E. Gounaris This paper contemplates the use of robust optimization as a framework for addressing problems that involve endogenous uncertainty, i.e., uncertainty that is affected by the decision maker’s strategy. To that end, we extend generic polyhedral uncertainty sets typically considered in robust optimization into sets that depend on the actual decisions. We present the derivation of robust counterpart models in this setting, and we discuss relevant algorithmic considerations for solving these models to guaranteed optimality. Besides capturing the functional changes in parameter correlations that may be induced by given decisions, we show how the use of our decision-dependent uncertainty sets allows us to also eradicate conservatism effects from parameters that become irrelevant in view of the optimal decisions. We quantify these benefits via a number of case studies, demonstrating our proposed framework’s versatility to be utilized in the context of various applications.
Available online 10 January 2018
Efficient numerical simulation of simulated moving bed chromatography with a single-column solver
Publication date: Available online 11 January 2018
Source:Computers & Chemical Engineering Author(s): Qiao-Le He, Samuel Leweke, Eric von Lieres We present four different numerical methods for the numerical simulation of simulated moving bed chromatography. Two approaches use fixed-point iteration for computing cyclic steady states, and two other approaches use operator splitting for computing complete system trajectories. All methods are based on weak coupling of individual column models and can easily be implemented using any existing single-column solver. Simulation software is implemented based on the CADET project and published as open source code. The numerical performance is compared using five case studies. For both fixed-point iteration and operator-splitting, an alternative approach is found to be more efficient than the standard approach. Namely, the one-column analog saves time in computing the cyclic steady state, while lag-aware operator-splitting yields more detailed information on the system trajectory. The presented methods can be combined with other models, for example to consider hold-up volumes, and have applications beyond simulated bed chromatography.
Available online 9 January 2018
Numerical Analysis of Accelerated Degradation in Large Lithium-ion Batteries
Publication date: Available online 10 January 2018
Source:Computers & Chemical Engineering Author(s): Hong-Keun Kim, Charn-Jung Kim, Chang-Wan Kim, Kyu-Jin Lee The size effect on degradation in lithium-ion battery cells is investigated by simulations of lithium iron phosphate/graphite LIB cells with different size. An electrical-electrochemical-thermal model considering degradation phenomena is modeled for a 1Ah pouch cell and a 55Ah pouch cell with an identical electrode design. Numerical results in the large cell shows the additional voltage drops of 27mV and the mean temperature increase of 8°C for 3C discharge due to overpotentials in metal current collectors and clear spatial imbalances of temperature, current density and electric potential. The capacity fade in the large cell is accelerated by about 33% for cycling operation due to the activated parasitic reactions at high temperature conditions. But even in an isothermal condition, the large cell still shows about 7% faster degradation than the small cell because it stays longer at high SOC in the charge process.
4 January 2018
Optimal Scheduling of Interconnected Power Systems
Publication date: Available online 9 January 2018
Source:Computers & Chemical Engineering Author(s): Nikolaos E. Koltsaklis, Ioannis Gioulekas, Michael C. Georgiadis This paper presents an optimization-based approach to address the problem of the optimal daily energy scheduling of interconnected power systems in electricity markets. More specifically, a Mixed Integer Linear Programming model (MILP) has been developed to address the specific challenges of the underlying problem. The main focus of the proposed framework is to examine the importance and the impacts of electricity interconnections and cross-border electricity trade on the scheduling of power systems, both at a technical and economic level. The applicability of the proposed approach has been tested on an illustrative case study including five power systems which can be interconnected (with a certain interconnection structure) or not. The proposed model determines in a detailed and analytical way the optimal power generation mix, the electricity trade among the systems, the electricity flows (in case of interconnection options), the marginal price of each system, as well as it investigates through a sensitivity analysis the effects of the available interconnection capacity on the resulting power production mix. The work demonstrates that the proposed optimization approach is able to provide important insights into the appropriate energy strategies followed by the market participants, as well as on the strategic long-term decisions to be implemented by investors and/or policy makers at a national and/or regional level, underlining potential risks and providing appropriate price signals on critical energy infrastructure projects under real market operating conditions.
4 January 2018
Editorial Board
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109

4 January 2018
Dynamic optimization of a cryogenic air separation unit using a derivative-free optimization approach
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Israel Negrellos-Ortiz, Antonio Flores-Tlacuahuac, Miguel Angel Guti
4 January 2018
Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Zahra Rafie-Majd, Seyed Hamid Reza Pasandideh, Bahman Naderi In this article a three-echelon supply chain, consisting of a supplier, a number of distribution centers (DCs), and a number of retailers (customers) is modeled in form of the integrated inventory- location – routing problem (ILRP), in a way that perishable products are delivered to the customers in a limited time horizon, consisting of several time periods. The retailers’ demand is stochastic and follows normal distribution with certain mean and standard deviation. The transportation fleet is heterogeneous, and distribution centers use a timetable, which will prevent interference of the vehicles operation and also allocation of a vehicle to more than one distribution center in each time period. Lagrangian Relaxation Method is used to solve the resulted model and determine the lower bound; and a heuristic algorithm is provided to feasibilize the result of the Lagrangian Relaxation Algorithm and determine the upper bound.
4 January 2018
Identification in dynamic networks
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Paul M.J. Van den Hof, Arne G. Dankers, Harm H.M. Weerts System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.
4 January 2018
Optimal scheduling and its Lyapunov stability for advanced load-following energy plants with CO2 capture
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Temitayo Bankole, Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E. Zitney In this study, a two-level control methodology consisting of an upper-level scheduler and a lower-level supervisory controller is proposed for an advanced load-following energy plant with CO2 capture. With the use of an economic objective function that considers fluctuation in electricity demand and price at the upper level, optimal scheduling of energy plant electricity production and carbon capture with respect to several carbon tax scenarios is implemented. The optimal operational profiles are then passed down to corresponding lower-level supervisory controllers designed using a methodological approach that balances control complexity with performance. Finally, it is shown how optimal carbon capture and electricity production rate profiles for an energy plant such as the integrated gasification combined cycle (IGCC) plant are affected by electricity demand and price fluctuations under different carbon tax scenarios. The paper also presents a Lyapunov stability analysis of the proposed scheme.
4 January 2018
Data-driven robust optimization under correlated uncertainty: A case study of production scheduling in ethylene plant
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Yi Zhang, Xuanzhi Jin, Yiping Feng, Gang Rong To hedge against the fluctuations generated from continuous production processes, practical solutions can be obtained through robust optimization induced by the classical uncertainty sets. However, uncertainties are sometimes correlated in industrial scheduling problems because of the connected process and various random factors. To capture and enrich the valid information of uncertainties, copulas are introduced to estimate the joint probability distribution and simulate mutual scenarios for uncertainties. Cutting planes are generated to remove unnecessary uncertain scenarios in the uncertainty sets, and then robust formulations induced by the cut set are proposed to reduce conservatism and improve the robustness of scheduling solutions. A real-world process of ethylene plant is introduced as the numerical case, and high-dimensional data-driven uncertainty sets are illustrated in detail. The proposed models are proved to control the fluctuation of consumed fuel gas below a lower level of conservatism.
4 January 2018
High-order approximation of chromatographic models using a nodal discontinuous Galerkin approach
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Kristian Meyer, Jakob K. Huusom, Jens Abildskov A nodal high-order discontinuous Galerkin finite element (DG-FE) method is presented to solve the equilibrium-dispersive model of chromatography with arbitrary high-order accuracy in space. The method can be considered a high-order extension to the total variation diminishing (TVD) framework used by Javeed et al. (2011a,b, 2013) with an efficient quadrature-free implementation. The framework is used to simulate linear and non-linear multicomponent chromatographic systems. The results confirm arbitrary high-order accuracy and demonstrate the potential for accuracy and speed-up gains obtainable by switching from low-order methods to high-order methods. The results reproduce an analytical solution and are in excellent agreement with numerical reference solutions already published in the literature.
4 January 2018
Improved quadratic cuts for convex mixed-integer nonlinear programs
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Lijie Su, Lixin Tang, David E. Bernal, Ignacio E. Grossmann This paper presents scaled quadratic cuts based on scaling the second-order Taylor expansion terms for the decomposition methods Outer Approximation and Partial Surrogate Cuts for solving convex Mixed Integer Nonlinear Programing problems. The scaled quadratic cut is proved to be a stricter and tighter underestimation for convex nonlinear functions than classical supporting hyperplanes, which results in the improvement of Outer Approximation and Partial Surrogate Cuts based solution methods. We integrate the strategies of scaled quadratic cuts with multi-generation cuts for Outer Approximation and Partial Surrogate Cuts and develop six types of Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts. These cuts are incorporated in the master problem of the decomposition methods leading to a Mixed Integer Quadratically Constrained Programming problem. Numerical results of benchmark Mixed Integer Nonlinear Programming problems demonstrate the effectiveness of the proposed Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts.
4 January 2018
Scenario tree reduction methods through clustering nodes
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Zhiping Chen, Zhe Yan To develop practical and efficient scenario tree reduction methods, we introduce a new methodology which depends on clustering nodes, and thus an easy-to-handle distance function to measure the difference between two scenario trees is designed. On the basis of minimizing the new distance, we construct a multiperiod scenario tree reduction model which is supported theoretically by the stability results of stochastic programs. By solving the model, we design a stage-wise scenario tree reduction algorithm which is superior to the simultaneous backward reduction method in terms of both computational complexity and solution results of stochastic programming problems, the corresponding reduction algorithm especially for fan-liked trees is also presented. We further design a multiperiod scenario tree reduction algorithm with a pre-specified distance by utilizing the stability results of stochastic programs. A series of numerical experiments with real trading data and the application to multiperiod portfolio selection problem demonstrate the practicality, efficiency and robustness of proposed reduction model and algorithms.
4 January 2018
Life cycle analysis of coal based methanol-to-olefins processes in China
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Dan Gao, Xu Qiu, Yuning Zhang, Pei Liu In the present paper, life cycle analysis of coal based methanol-to-olefins processes in China is performed based on the detailed information of the china’s largest project of its kind. The purpose of our analysis is to identify the reduction potentials of the project for the energy/water saving and the emission control. The details of the project are given together with the involved techniques. In our analysis, the water and energy consumptions, CO2/SO2/NOx emissions are all demonstrated in terms of six sub-processes with both the direct and indirect contributions. Based on the analysis, we identify that the coal-to-methanol process consumes a vast amount of water and energy with significant CO2/SO2/NOx emissions. For water/energy savings, methanol-to-olefins process is of litter potential because its consumptions are mainly the indirect ones. The negative effects of CCS should be noticed for the implement in the large-scale coal based chemical engineering due to its significant consumptions of the water and energy.
4 January 2018
A process simulator interface for multiobjective optimization of chemical processes
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Carlos Andr
4 January 2018
DMFA-based operation model for fermentation processes
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Yan Gao, Zhonggai Zhao, Fei Liu The existing dynamical fermentation models only reveal approximate macroscopic properties involving no internal mechanism, while dynamic metabolic flux analysis (DMFA) provides internal microscopic dynamic features, but without regulation from external operation conditions. This is the first attempt to bridge the mapping between macro operation variables and micro metabolic fluxes. Based on the macro-micro mapping relationship, a new operation model was constructed, which can use macro operation variables to regulate micro metabolic fluxes. Firstly, metabolic network was analyzed based on DMFA to derive flux distribution. Next, the fluxes defined as outputs were related to macro operation variables to establish the operation model. The complexity of cellular growth and diversity of flux distribution led to nonlinear and multi-stage characteristics of the mapping relationship, and thus a multi-model modeling method was employed as key algorithm. Finally, a simulation and a lab-scale experiment were conducted to demonstrate the application of the proposed method.
4 January 2018
Optimal operation of a Solar Membrane Distillation pilot plant via Nonlinear Model Predictive Control
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Juan D. Gil, Lidia Roca, Alba Ruiz-Aguirre, Guillermo Zaragoza, Manuel Berenguel Solar Membrane Distillation (SMD) is an under-investigation desalination process suitable for developing self-sufficient small scale applications. The use of solar energy considerably reduces the operating costs, however, its intermittent nature requires a non-stationary optimal operation that can be achieved by means of advanced control strategies. In this paper, a hierarchical control system composed by two layers is used for optimizing the operation of a SMD pilot plant, in terms of thermal efficiency, distillate production and cost savings. The upper layer is formed by a Nonlinear Model Predictive Control (NMPC) scheme that allows us to obtain the optimal operation by optimizing the solar energy use. The lower layer includes a direct control system, in charge of attaining the variable references provided by the upper layer. Simulation and experimental tests are included and commented in order to demonstrate the benefits of the developed control system.
4 January 2018
3D modeling of a CPOX-reformer including detailed chemistry and radiation effects with DUO
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Matthias Hettel, Eric Daymo, Olaf Deutschmann The impact of radiation heat transfer and radial heat losses in small-scale monoliths, as often used for testing catalysts or qualifying process conditions, are an important consideration to design and predict performance of commercial size reactors. The paper presents the 3D modeling of a honeycomb CPOX (Catalytic Partial Oxidation) reformer, including detailed surface chemistry for the conversion of methane on rhodium. The calculation domain comprises the flow region and two monoliths (one of them coated) which are positioned in a glass tube. For the simulations the software tool DUO (coupling between OpenFOAM and DETCHEM™) was used. The objective was to model the system without any boundary conditions for the temperature (aside from the inlet). As the temperature level is above 900K solid body radiation has to be included. The comparison of the results with detailed experimental data shows that it is possible to reproduce the species concentrations and the temperature fields of the flow and solid structures well. The effect of radiation, leading to a heat transfer between the two monoliths, can clearly be indicated. However, this effect plays only a minor role with respect to the chemical conversion. The simulations capture the measured effect of radial heat removal on the conversion process in different channels inside the catalyst.
4 January 2018
Maximal safe set computation for pressure swing adsorption processes
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Mohammad Fakhroleslam, Shohreh Fatemi, Ramin Bozorgmehry Boozarjomehry, Elena De Santis, Maria Domenica Di Benedetto, Giordano Pola In this paper we propose a method towards purity control of pressure swing adsorption (PSA) processes which is based on the use of hybrid systems formalism. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe in detail PSA processes. Based on mechanistic model of the processes, a local reduced-order model (LROM) is developed for PSA processes. Then the processes are represented as hybrid systems whose continuous evolution is described by the LROM. We then perform an analysis of hybrid reachability properties of the hybrid system obtained, based on which the so-called maximal safe set is computed. The analysis is performed for a two-bed, six-step benchmark PSA process and the influence of the control inputs and external disturbances are investigated.
4 January 2018
Integration of Fuzzy Analytic Hierarchy Process into multi-objective Computer Aided Molecular Design
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Jecksin Ooi, Michael Angelo B. Promentilla, Raymond R. Tan, Denny K.S. Ng, Nishanth G. Chemmangattuvalappil In this paper, a novel Computer Aided Molecular Design (CAMD) framework is developed to solve multi-objective molecular design problems. CAMD can be formulated as a multi-objective optimisation problem when there are multiple target properties to be optimised simultaneously. A major obstacle faced by multi-objective CAMD problems is the difficulty in assigning weighting factors to the target properties, since the relative importance of these factors is not always defined. It is particularly difficult to compare target properties which belong to different categories, such as physicochemical, safety, health and environmental properties, on a common scale. This paper presents a systematic CAMD algorithm built on Fuzzy Analytic Hierarchy Process (FAHP) to deal with the ambiguity involved in evaluating the weights of target properties in multi-objective CAMD problem. Instead of using exact numerical values, FAHP approach expresses the pairwise comparison of target properties through triangular fuzzy numbers, which allow the degree of confidence of decision maker to be quantified. Hence, the proposed approach can address the uncertainties arising from ambiguity involved during value judgement elicitation in multi-objective CAMD problems. The solutions generated provide a better balance of performance for a set of identified target properties. The proposed methodology is illustrated through a case study on designing a better solvent for extracting residual oil from palm pressed fibre.
4 January 2018
Modelling and optimization of a moving-bed adsorptive reactor for the reverse water-gas shift reaction
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Alejandro A. Munera Parra, Carsten Asmanoglo, David W. Agar In this work, a novel variant of the reverse water-gas shift reaction is proposed as a promising route to valorize CO2 as syngas. The reactor concept used is that of an adsorptive moving-bed in order to permit low-temperature operation with high conversions and to improve upon the fixed-bed adsorptive concept previously investigated. The reactor has been modelled for several configurations and subsequently optimized. The results show that an increase up to an order of magnitude in the space-time-yield (STY) is possible by using the moving-bed configuration in comparison to fixed-bed operation. Finally, a bi-objective optimization is carried out to identify the trade-off between operating at higher STY and higher adsorbent loadings.
4 January 2018
Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Yizhong Chen, Li He, Jing Li, Shiyue Zhang One of the critical problems in cooperative shale gas supply chains and production systems design is life cycle optimization of the economic and environmental performance under uncertainty. This study develops an inexact multi-criteria decision making (IMCDM) model with consideration of shale gas production profiles and recoverable reserves. The IMCDM framework is based on an integration of life cycle analysis, interval linear programming, multi-objective programming, and multi-criteria decision analysis approaches. An application to the Marcellus Shale supply chains is presented to demonstrate capabilities and effectiveness of the developed model, where the future spread in shale gas output follows from the variation in drilled well counts according to different scenarios. Design and operational decisions with respect to well drilling schedule, shale gas production, freshwater supply, wastewater disposal, and greenhouse gas (GHG) emissions are then generated. An optimal strategy is further provided for stakeholders after evaluation of the trade-off among multiple criteria.

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4 January 2018
Parameter estimation of models with limit cycle based on the reformulation of the objective function
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Andressa Apio, Viviane R. Botelho, Jorge O. Trierweiler Many processes show limit cycles, meaning that the system presents oscillatory behavior. The parameter estimation of such kind of systems is not a simple task, due to the non-convexity of the optimization problem. This paper proposes the inclusion of a driving term based on the damping factor in the classical objective function formulation, reducing the non-convexity of the problem. This driving term is reduced after each iteration until its complete elimination, as the system starts to have oscillatory behavior close to the limit cycle. Two case studies illustrate the strengths of the proposed approach: the J
4 January 2018
Model predictive control with closed-loop re-identification
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Masoud Kheradmandi, Prashant Mhaskar In this work, we address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are then the new model is utilized within the MPC. The effectiveness of the proposed method is illustrated through simulations on a nonlinear polymerization reactor.
4 January 2018
Monte-Carlo-simulation-based optimization for copolymerization processes with embedded chemical composition distribution
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Yannan Ma, Xi Chen, Lorenz T. Biegler As chemical composition distribution (CCD) is a crucial microstructural quality index of copolymers, optimization of operating policies using CCD is of great importance. Monte Carlo simulation is an efficient method to calculate the CCD that cannot be easily determined by traditional equation-based methods But this method is computationally expensive. In this project, we first propose a parallel technique to conduct the Monte Carlo simulation on the graphics processing unit (GPU) platform. Additionally, an adaptive simulation algorithm is proposed to reduce computational cost based on error estimation of the Monte Carlo simulation. Considering the uncertainties in the Monte Carlo simulation, derivative-free method is applied for the CCD-target optimization. A successive boundary shrinkage (SBS) formulation is developed to improve the convergence of problem solving. The above-mentioned methods are successfully integrated and implemented on the optimization of a copolymerization process with high efficiency and good performance.
4 January 2018
Support vector regression modelling and optimization of energy consumption in carbon fiber production line
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Gelayol Golkarnarenji, Minoo Naebe, Khashayar Badii, Abbas S. Milani, Reza N. Jazar, Hamid Khayyam The main chemical industrial efforts are to systematically and continuously explore innovative computing methods of optimizing manufacturing processes to provide better production quality with lowest cost. Carbon fiber industry is one of the industries seeks these methods as it provides high production quality while consuming a lot of energy and being costly. This is due to the fact that the thermal stabilization process consumes a considerable amount of energy. Hence, the aim of this study is to develop an intelligent predictive model for energy consumption in thermal stabilization process, considering production quality and controlling stochastic defects. The developed and optimized support vector regression (SVR) prediction model combined with genetic algorithm (GA) optimizer yielded a very satisfactory set-up, reducing the energy consumption by up to 43%, under both physical property and skin-core defect constraints. The developed stochastic-SVR-GA approach with limited training data-set offers reduction of energy consumption for similar chemical industries, including carbon fiber manufacturing.
4 January 2018
Development of a recursive time series model for fed-batch mammalian cell culture
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Jingwei Gan, Satish J. Parulekar, Ali Cinar Recursive time series models are developed in this work for a fed-batch mammalian cell culture producing monoclonal antibodies, with key culture variables measured at different sampling frequencies. Glucose and glutamine feed rates are considered as inputs. A composite of an autoregressive moving average with exogenous input model and a dual rate-autoregressive with exogenous input model is used. Appropriate parameter constraints are imposed in parameter estimation algorithms and stability of these is examined and ensured. The data required for parameter estimation are generated from simulated fed-batch experiments using a well-tested first principles model. The predictions for glucose, glutamine, and viable cell concentrations track very well the data for these, with the errors for the high prediction horizons considered being limited to 10% or less. The prediction accuracy can be increased further if data from prior experiments with dynamic similarities are available. The models can be used reliably for model predictive control.
4 January 2018
On the effect of price policies in the design of formulated products
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Mariano Mart

Fault detection based on augmented kernel Mahalanobis distance for nonlinear dynamic processes
Publication date: 4 January 2018
Source:Computers & Chemical Engineering, Volume 109 Author(s): Jun Shang, Maoyin Chen, Hanwen Zhang This paper presents a fault detection method based on augmented kernel Mahalanobis distance (AKMD) for monitoring nonlinear dynamic processes. In order to reflect the information of dynamic correlations, the measurements are stacked into augmented vectors at adjacent sampling instants. The augmented kernel Mahalanobis distance serves as the detection index, and its control limit is determined by the empirical method with assigning a significance level. Contrary to the mainstream of process monitoring methods based on principal component analysis (PCA), dimensionality reduction is not used here. The disadvantage of dimensionality reduction and space partition is discussed, and the improvement of fault detectability via data augmentation is analyzed. In addition, the computational complexity of the proposed method is acceptable. For training dataset containing m variables and n samples, if n
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