Optimal Operation of Parallel Distillation Systems with Multiple Product Grades: An Industrial Case Study Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-17 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.
Robust Optimization for Decision-making under Endogenous Uncertainty Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-13 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.
Efficient numerical simulation of simulated moving bed chromatography with a single-column solver Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-11 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.
Numerical Analysis of Accelerated Degradation in Large Lithium-ion Batteries Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-10 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.
Optimal Scheduling of Interconnected Power Systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-09 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.
A Novel Approach to Process Operating Mode Diagnosis Using Conditional Random Fields in the Presence of Missing Data Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-04 Mengqi Fang, Hariprasad Kodamana, Biao Huang, Nima Sammaknejad
Diagnosis of process operating modes is an important aspect of process monitoring. Due to its ability to model process transitions, the Hidden Markov Model (HMM) is widely used as a tool for operating mode diagnosis. However, it suffers from certain drawbacks due to its inherent assumptions. Hence, to address these issues and improve the operating mode diagnosis performance, we introduce the Conditional Random Field (CRF), which is a discriminiative probabilistic model based approach. Further, to deal with the missing measurement problem that commonly occurs in industrial datasets, a marginalized CRF framework is proposed in this paper and the related inference algorithms are developed under this newly designed framework. Validation studies performed on a simulated continuous stirred tank reactor (CSTR) system and an experimental hybrid tank system demonstrate that the proposed CRF based algorithms have superior performances compared to the existing approaches.
State estimation of wastewater treatment plants based on model approximation Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-04 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.
Collection of Benchmark Test Problems for Data Reconciliation and Gross Error Detection and Identification Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-04 Edson Cordeiro do Valle, Ricardo de Araújo Kalid, Argimiro Resende Secchi, Asher Kiperstok
In an industrial scenario, one can find measured data that do not satisfy the mass and energy laws of conservation. This problem can be approached by applying data reconciliation (DR) and gross error detection and identification (GEDI) techniques, however, authors generally validate their methods using a reduced set of problems, restricting the application of the proposed methods to them. The objective of this work is to present a collection of benchmark problems for DR and GEDI to help the evaluation of these methods in different types of flowsheets. First, challenges issues related with DR and GED are presented with examples. Then, a general overview of the benchmark collection set is presented. In conclusion, it can be observed that this challenging research area needs a common problem set for validating DR and GEDI and this paper fills this gap, helping the validation of the methods.
Bounded-Error Optimal Experimental Design via Global Solution of Constrained Min-Max Program Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-04 Olga Walz, Hatim Djelassi, Adrian Caspari, Alexander Mitsos
A Systematic Approach for Modeling of Waterflooding Process in the Presence of Geological Uncertainties in Oil Reservoirs Comput. Chem. Eng. (IF 3.024) Pub Date : 2018-01-03 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.
Control of Heat-Integrated Extractive Distillation Processes Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-29 William L. Luyben
Pressure in many distillation columns is set such that cooling water can be used in the condenser. Pressure selection is more involved in some columns such as reactive distillation in which there is a trade-off between temperatures favorable for reaction kinetics and temperatures favorable for vapor-liquid equilibrium. In azeotropic systems, pressure selection is critical in achieving the desired separation by consideration of distillation boundaries and isovolatility curves. A recent paper presented a striking example of pressure selection in extractive distillation. Operation at 1 atm required a solvent-to-feed (S/F) ratio of 3.52 while operating at 10 atm cut the S/F to only 0.717.The purpose of this paper is to explore the dynamic controllability of this low S/F extractive distillation system. Results show that the control structure must be modified from the conventional configuration used in most extractive distillation processes.
A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-29 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.
Chemical Process Systems Analysis Using Thermodynamic Balance Equations with Entropy Generation. Revaluation and Extension. Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-29 John P. O'Connell
Integrated scheduling of rolling sector in steel production with consideration of energy consumption under time-of-use electricity prices Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-29 Shengnan Zhao, Ignacio E. Grossmann, Lixin Tang
Due to increasing load and penetration of renewable, 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.
PROCESS MODELLING, SIMULATION AND TECHNOECONOMIC EVALUATION OF CRYSTALLISATION ANTISOLVENTS FOR THE CONTINUOUS PHARMACEUTICAL MANUFACTURING OF RUFINAMIDE Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-28 Samir Diab, Dimitrios I. Gerogiorgis
Data-Driven Stochastic Robust Optimization: General Computational Framework and Algorithm Leveraging Machine Learning for Optimization under Uncertainty in the Big Data Era Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-28 Chao Ning, Fengqi You
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.
Rigorous Design of Reaction-Separation Processes Using Disjunctive Programming Models Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-28 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.
A cost-effective retrofit of conventional distillation sequence to dividing-wall prefractionator configuration Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-26 Le Quang Minh, Tram Ngoc Pham, Nguyen Van Duc Long, Joonho Shin, Moonyong Lee
Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-24 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.
A Trust Region-based Two Phase Algorithm for Constrained Black-box and Grey-box Optimization with Infeasible Initial Point Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-24 Ishan Bajaj, Shachit S. Iyer, M.M.Faruque Hasan
This paper presents an algorithm for constrained black-box and grey-box optimization. It is based on surrogate models developed using input-output data in a trust-region framework. Unlike many current methods, the proposed approach does not require feasible initial point and can handle hard constraints via a novel optimization-based constrained sampling scheme. A two-phase strategy is employed, where the first phase involves finding feasible point through minimizing a smooth constraint violation function (feasibility phase). The second phase improves the objective in the feasible region using the solution of the feasibility phase as starting point (optimization phase). The method is applied to solve 92 test problems and the performance is compared with established derivative-free solvers. The two-phase algorithm outperforms these solvers in terms of number of problems solved and number of samples used. We also apply the algorithm to solve a chemical process design problem involving highly-coupled, nonlinear algebraic and partial differential equations.
CHALLENGES AND OPPORTUNITIES IN BIOPHARMACEUTICAL MANUFACTURING CONTROL Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-21 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.
Robust Integrated Production-Maintenance Scheduling for an Evaporation Network Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-21 C.G. Palacín, J.L. Pitarch, C. Jasch, C.A. Méndez, C. de Prada
Heat Exchanger Network Cleaning Scheduling: From Optimal Control to Mixed-Integer Decision Making Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-20 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.
Efficient simulation of chromatographic separation processes Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-20 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 upto 90 %, without degradation of the solution, relative to an equivalent uniform grid.
Combining the Advantages of Discrete- and Continuous-time Scheduling Models: Part 1. Framework and Mathematical Formulations Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-13 Hojae Lee, Christos T. Maravelias
We propose a general method for the solution of chemical production scheduling problems in network environments. The method consists of three stages. In the first stage, a discrete-time mixed-integer programming (MIP) model is solved to quickly obtain an approximate solution. In the second stage, the solution is mapped onto newly introduced unit- and material-specific continuous-time grids, using a mapping algorithm. In the third stage, a continuous-time linear programming (LP) model is solved to improve the accuracy of the mapped discrete-time solution by refining the timing of events and batch sizes. The proposed method takes advantage of the complementary strengths of discrete- and continuous-time formulations, which enables us to not only handle various processing features (e.g., intermediate deliveries and orders, time-varying resource availability and cost, variable processing times), but also obtain order of magnitude speedups in the solution of large-scale instances.
Global Optimisation of Multi-Plant Manganese Alloy Production Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-12 Martin Naterstad Digernes, Lars Rudi, Henrik Andersson, Magnus Stålhane, Stein O. Wasbø, Brage Rugstad Knudsen
This paper studies the problem of multi-plant manganese alloy production. The problem consists of finding the optimal furnace feed of ores, fluxes, coke, and slag that yields output products which meet customer specifications, and to optimally decide the volume, composition, and allocation of the slag. To solve the problem, a nonlinear pooling problem formulation is presented upon which the bilinear terms are reformulated using the Multiparametric Disaggregation Technique (MDT). This enables global optimisation by means of commercial software for mixed integer linear programs. We demonstrate the model and solution approach through case studies from a Norwegian manganese alloy producer. The computational study shows that the model and proposed optimisation approach can solve problem sizes of up to ten furnaces to a small optimality gap, that global optimization approach with MDT scales well with larger, real problem instances, and that the model outperforms the current operational practice.
Distributionally Robust Optimization for Planning and Scheduling under Uncertainty Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-08 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.
A multifluid-PBE model for simulation of mass transfer limited processes operated in bubble columns Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-12-06 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 on 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.
An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-28 Morgan T. Kelley, Richard C. Pattison, Ross Baldick, Michael Baldea
Today's emphasis 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.
A process systems approach for detailed rail planning and scheduling applications Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-24 Danielle Zyngier, Jan Lategan, Ludwig Furstenberg
Value chain integration is an ongoing challenge: while computing power has improved, there is little modeling consistency across the system. This paper bridges this gap by proposing a novel formulation for train scheduling, a linking element of value chains, using the Unit-Operation-Port-State Superstructure (UOPSS). Train scheduling is a challenging problem: rail lines can be hundreds of kilometers long with train crossing strategies that are based on a train station level, while also requiring results with a minute-time scale resolution. In mixed-use rail systems with limited passing loop infrastructure, trains have different passing priorities and lengths, thus differing in their ability to use passing loops. The proposed model is the basis of the Hatch Rail Optimizer (HRO) software. In addition to small case studies, the power of HRO is demonstrated through a practical case study involving a 370 km rail corridor with five different train sizes over a week-long scheduling horizon.
A novel robust optimization approach for an integrated municipal water distribution system design under uncertainty: A case study of Mashhad Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-23 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.
A two-stage procedure for the optimal sizing and placement of grid-level energy storage Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-22 Oluwasanmi Adeodu, Donald J. Chmielewski
The economic benefit realized from energy storage units on the electric grid is linked to the control policy selected to govern grid operations. Thus, the optimal sizing and placement (OSP) of such units is also dependent on the operating policy of the power network. In this work, we first introduce economic model predictive control (EMPC) as a viable economic dispatch policy for transmission networks with energy storage. However, the numeric basis of EMPC makes it ill-suited for the OSP problem. In contrast, the method of economic linear optimal control (ELOC) can be easily adapted to the OSP problem. However, the relaxation of point-wise-in-time constraints, inherent to ELOC, will introduce a systematic underestimation of operating costs. Thus, we introduce a 2-step OSP algorithm that begins with the ELOC-based approach to determine the placement of energy storage units. Then, an EMPC-based gradient search is used to determine optimal sizes.
Economic MPC and Real-time Decision Making with Application to Large-Scale HVAC Energy Systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-16 James B. Rawlings, Nishith R. Patel, Michael J. Risbeck, Christos T. Maravelias, Michael J. Wenzel, Robert D. Turney
With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We present a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed $485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications.
Application of formal verification and falsification to large-scale chemical plant automation systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-16 Blake C. Rawlings, John M. Wassick, B. Erik Ydstie
In this paper, we apply formal verification and falsification of temporal logic specifications to analyze chemical plant automation systems. We present new results, obtained by applying a recently-developed approach to handle combined invariance and reachability requirements. In addition, we develop a set of tests that can be generated automatically for a given control system, some of which have the same form as those in the existing literature, and some of which combine invariance and reachability, to which we apply the new approach mentioned previously. In both cases, we work with abstractions of the automation systems in order to apply symbolic model checking to industrial-scale problems. We demonstrate the results using a series of small illustrative examples, and also report results from an industrial case study. The methods that we apply are implemented in a pair of open-source software tools, which we describe briefly.
Resilient design and operations of process systems: Nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-13 Jian Gong, Fengqi You
This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system.
Modeling of hydraulic fracturing and designing of online pumping schedules to achieve uniform proppant concentration in conventional oil reservoirs Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-11 Prashanth Siddhamshetty, Seeyub Yang, Joseph Sang-Il Kwon
We present a novel control framework for the closed-loop operation of a hydraulic fracturing process. Initially, we focus on the development of a first-principle model of a hydraulic fracturing process. Second, a novel numerical scheme is developed to efficiently solve the coupled partial differential equations defined over a time-dependent spatial domain. Third, a reduced-order model is constructed, which is used to design a Kalman filter to accurately estimate unmeasurable states. Lastly, model predictive control theory is applied for the design of a feedback control system to achieve uniform proppant concentration across the fracture at the end of pumping by explicitly taking into account the desired fracture geometry, total amount of proppant injected, actuator limitations, and safety considerations. We demonstrate that the proposed control scheme is able to generate a spatial concentration profile which is uniform and close to the target concentration compared to that of the benchmark, Nolte's pumping schedule.
A framework for modeling and optimizing dynamic systems under uncertainty Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-11 Bethany Nicholson, John Siirola
Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.
Smart manufacturing and energy systems Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-07 Thomas F. Edgar, Efstratios N. Pistikopoulos
While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are not pervasive in industry. A vendor-agnostic SM platform is under development that integrates information technology, models, and simulations driven by real-time plant data and performance metrics. By utilizing existing process control and automation systems, manufacturing organizations can manage systems at a much lower cost, optimizing process knowledge and improving energy productivity. Three case studies are presented: steam methane reforming to make hydrogen, optimization of a heat treatment furnace for metals processing, and a fuel cell system, all of which utilize high fidelity models as a starting point for optimization and control. The Smart Manufacturing Leadership Coalition has led the national effort in SM, and the recently established National Manufacturing Innovation Institute funded by DOE, private industry, and state governments will be described.
Solving global optimization problems using reformulations and signomial transformations Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-06 A. Lundell, T. Westerlund
In this paper, a framework for reformulating nonconvex mixed-integer nonlinear programming (MINLP) problems containing twice-differentiable (C2) functions to convex relaxed form is discussed. To provide flexibility and for utilizing more effective transformation strategies, the twice-differentiable functions can be partitioned into convex, signomial and general nonconvex functions. The latter two can then be convexified using lifting transformations in combination with approximations using piecewise linear functions (PLFs). However, since there are many degrees of freedom in how to select the set of transformations, an optimization-based method is proposed for finding an optimal set. The lifting transformations are based on single-variable power and exponential transformations for signomials. For nonconvex C2-functions the α reformulation (αR) technique as well as more generally the method of difference of convex functions can be applied. In the αR, the αBB convex underestimator can be used. The framework is utilized in the α signomial global optimization (αSGO) algorithm to find the ϵ-global solution to a nonconvex problem by iteratively updating the approximations provided by the PLFs. The framework can also be used to directly obtain a convex relaxation of any nonconvex MINLP problem of the specified type to a determined accuracy.
Process operational safety via model predictive control: Recent results and future research directions Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-06 Fahad Albalawi, Helen Durand, Panagiotis D. Christofides
The concept of maintaining or enhancing chemical process safety encompasses a broad set of considerations which stem from management/company culture, operator procedures, and engineering designs, and are meant to prevent incidents at chemical plants. The features of a plant design that take action to prevent incidents on a moment-by-moment basis are the control system and the safety system (i.e., the alarm system, safety instrumented system, and safety relief system). Though the control and safety systems have a common goal in this regard, coordination between them has been minimal. One impediment to such an integrated control-safety system design is that the traditional industrial approach to safety focuses on root causes of incidents and on keeping individual measured variables within recommended ranges, rather than seeking to understand incidents from a more fundamental perspective as the result of the dynamic process state evolving to a value at which consequences to humans and the environment occur. This work reviews the state of the art in control system designs that incorporate explicit safety considerations in the sense that they have constraints designed to prevent the process state from taking values at which incidents can occur and in the sense that they are coordinated with the safety system. The intent of this tutorial is to unify recent developments in this area and to encourage further research by showcasing that the topic, though critical for safe operation of chemical processes particularly as we move to more tightly integrated and economics-focused operating strategies, is in its infancy and that many open questions remain.
Municipal solid waste to liquid transportation fuels – Part III: An optimization-based nationwide supply chain management framework Comput. Chem. Eng. (IF 3.024) 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.
Stochastic model predictive control — how does it work? Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-05 Tor Aksel N. Heirung, Joel A. Paulson, Jared O’Leary, Ali Mesbah
Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance constraints, which enables a systematic trade-off between attainable control performance and probability of state constraint violations in a stochastic setting. This paper presents an overview of core concepts in SMPC in relation to MPC and stochastic optimal control, with numerical illustrations on a typical chemical process. Estimation of stochastic disturbances as well as the impact of estimation quality of stochastic disturbances on the SMPC performance are discussed. Some avenues for future research in SMPC are suggested.
Nonsmooth differential-algebraic equations in chemical engineering Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-11-05 Peter Stechlinski, Michael Patrascu, Paul I. Barton
This article advocates a nonsmooth differential-algebraic equations (DAEs) modeling paradigm for dynamic simulation and optimization of process operations. A variety of systems encountered in chemical engineering are traditionally viewed as exhibiting hybrid continuous and discrete behavior. In many cases such discrete behavior is nonsmooth (i.e. continuous but nondifferentiable) rather than discontinuous, and is appropriately modeled by nonsmooth DAEs. A computationally relevant theory of nonsmooth DAEs (i.e. well-posedness and sensitivity analysis) has recently been established (Stechlinski and Barton, 2016a, 2017) which is suitable for numerical implementations that scale efficiently for large-scale dynamic optimization problems. Challenges posed by competing hybrid modeling approaches for process operations (e.g. hybrid automata) are highlighted as motivation for the nonsmooth DAEs approach. Several examples of process operations modeled as nonsmooth DAEs are given to illustrate their wide applicability before presenting the appropriate mathematical theory.
Analysis of the multiplicity of steady-state profiles of two tubular reactor models Comput. Chem. Eng. (IF 3.024) 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.
Dynamic latent variable analytics for process operations and control Comput. Chem. Eng. (IF 3.024) 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.
The impact of digitalization on the future of control and operations Comput. Chem. Eng. (IF 3.024) 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.
A Multitree Approach for Global Solution of ACOPF Problems Using Piecewise Outer Approximations Comput. Chem. Eng. (IF 3.024) 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.
On improving the online performance of production scheduling: Application to air separation units Comput. Chem. Eng. (IF 3.024) 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.
Advanced optimization strategies for integrated dynamic process operations Comput. Chem. Eng. (IF 3.024) 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.
An algorithm for gradient-based dynamic optimization of UV flash processes Comput. Chem. Eng. (IF 3.024) 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.
Decomposing complex plants for distributed control: Perspectives from network theory Comput. Chem. Eng. (IF 3.024) 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.
Scheduling, optimization and control of power for industrial cogeneration plants Comput. Chem. Eng. (IF 3.024) 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.
Petroleum production optimization – A static or dynamic problem? Comput. Chem. Eng. (IF 3.024) 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.
Machine learning: Overview of the recent progresses and implications for the process systems engineering field Comput. Chem. Eng. (IF 3.024) 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.
Framework for a smart data analytics platform towards process monitoring and alarm management Comput. Chem. Eng. (IF 3.024) 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.
An optimization based strategy for crude selection in a refinery with lube hydro-processing Comput. Chem. Eng. (IF 3.024) 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.
On decoupling rate processes in chemical reaction systems – Methods and applications Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-10-07 Julien Billeter, Diogo Rodrigues, Sriniketh Srinivasan, Michael Amrhein, Dominique Bonvin
Models of chemical reaction systems can be complex as they need to include information regarding the reactions and the mass and heat transfers. The commonly used state variables, namely, concentrations and temperatures, express the interplay between many phenomena. As a consequence, each state variable is affected by several rate processes. On the other hand, it is well known that it is possible to partition the state space into a reaction invariant subspace and its orthogonal complement using a linear transformation involving the reaction stoichiometry. This paper uses a more sophisticated linear transformation to partition the state space into various subspaces, each one linked to a single rate process such as a particular reaction, mass transfer or heat transfer. The implications of this partitioning are discussed with respect to several applications related to data reconciliation, state and rate estimation, modeling, identification, control and optimization of reaction systems.
Parallel cyclic reduction decomposition for dynamic optimization problems Comput. Chem. Eng. (IF 3.024) 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.
Economic opportunities for industrial systems from frequency regulation markets Comput. Chem. Eng. (IF 3.024) Pub Date : 2017-09-23 Alexander W. Dowling, Victor M. Zavala
We analyze economic opportunities for industrial facilities provided by frequency regulation (FR) markets. We use classical frequency domain analysis techniques to characterize the harmonic content of FR signals and to analyze the impact of such harmonics on the response of dynamical systems. The analysis reveals that systems with slow dynamics, as those found in large industrial facilities, are suitable to provide FR capacity because they can naturally damp the dominant high-frequency harmonic content of FR signals. We also propose optimization formulations to quantify the maximum amount of FR capacity that can be provided by a system given its dynamic characteristics, its control architecture. A distillation case study demonstrates that significant economic potential exists for large industrial facilities.
Generalized robust counterparts for constraints with bounded and unbounded uncertain parameters Comput. Chem. Eng. (IF 3.024) 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.
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