Fuzzy optimization design of multicomponent refinery hydrogen network

Hydrogen and light hydrocarbon components are essential resources of the refinery. The optimization of the refinery hydrogen system and recovery of the light hydrocarbon components contained in the gas streams are key strategies to reduce the operating costs for sustainable development. Many research efforts have been focused on the optimization of single impurity hydrogen network, and the flowrates of the hydrogen sources and sinks are assumed to be constant. However, their flowrates vary along with the quality of crude oil and refinery processing plans. A general superstructure of multicomponent refinery hydrogen network is proposed, which considers four components, namely H₂, H₂S, CH₄ and , as well as the flowrate variations of hydrogen source and hydrogen sink. The mathematical model based on the superstructure is developed with objective functions, including the minimization of total annualized cost and the maximization of overall satisfaction of the hydrogen network. Moreover, the model considers the removal of hydrogen sulfide and the recovery of light hydrocarbon components (i.e.C₂₊, ) in the optimization. To verify the applicability of the proposed mathematical model, a simplified industrial case study with four scenarios is solved. The optimization results show that the economic benefit can be maximized by considering both the direct reuse of gas streams from high-pressure separator (HP gas stream) and from low-pressure separator (LP gas stream) and the recovery of the light hydrocarbon streams. The fuzzy optimization method can be used to guide the optimal design of the refinery hydrogen system with multi-period variable flowrates.     

Economic model predictive control with time‐varying objective function for nonlinear process systems

Economic model predictive control (EMPC) is a control scheme that combines real‐time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time‐varying process economics). In the present work, we focus on the development of a Lyapunov‐based EMPC (LEMPC) scheme that is formulated with an explicitly time‐varying economic cost function. First, the formulation of the proposed two‐mode LEMPC is given. Second, closed‐loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed‐loop simulations of a chemical process that the proposed LEMPC can achieve stability with time‐varying economic cost as well as improve economic performance of the process over a conventional MPC scheme. © 2013 American Institute of Chemical Engineers AIChE J 60: 507–519, 2014     

Integration of Design and Control: A Robust Control Approach Using MPC

This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.     

Optimization of grade transitions in polyethylene solution polymerization processes

This study considers the development of optimization models for grade transition of polyethylene solution polymerization processes. A detailed mathematical model is developed to capture the dynamics of the solution polymerization process. This includes time delay models for vapor and liquid recycle streams as well as a reduced, yet accurate, vapor‐liquid equilibrium (VLE) model derived from rigorous VLE calculations. Simultaneous dynamic optimization approach is applied to solve the optimization problem to reduce off‐spec production time and transition time. Two optimization formulations, single stage and multistage, are developed to deal with single‐value target and specification bands of product properties, respectively. The results show significant reductions in grade transition time and off‐spec production time. In addition, the multistage formulation designed for problems with specification bands outperforms its single stage counterpart. It minimizes transition time and off‐spec production directly, and leads to higher performance control profiles. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1126–1142, 2016     

Modeling,simulation, and optimization of multiproduct cryogenic air separation unit startup

The startup of multiproduct cryogenic air separation plants takes several hours, during which time limited revenue is generated with high costs incurred due to the highly energy-intensive nature of these operations. This motivates the development of high-fidelity dynamic models to capture the complexity of the startup process to aid decision-making. This article focuses on the development of a startup model for a multiproduct air separation unit (ASU), and its use in dynamic simulation and optimization. To accomplish this, a first-principles based dynamic ASU model is extended by including various discontinuities using smooth approximations, adding dynamics to the primary heat exchanger, and extending the handling phase change within process streams. Dynamic simulations demonstrate plant response behavior during startup, including a failed startup resulting from an injudicious choice of input trajectory. In addition, improvement of startup operation is demonstrated through the incorporation of the model within a dynamic optimization framework.     

Reference Trajectory Optimization Under Constrained Predictive Control

Chemical process systems often need to respond to frequently changing product demands. This motivates the determination of optimal transitions, subject to specification and operational constraints. However, direct implementation of optimal input trajectories would, in general, result in offset in the presence of disturbances and plant/model mismatch. This paper considers reference trajectory optimization of processes controlled by constrained model predictive control (MPC). Consideration of the closed‐loop dynamics of the MPC‐controlled process in the reference trajectory optimization results in a multi‐level optimization problem. A solution strategy is applied in which the MPC quadratic programming subproblems are replaced by their Karush‐Kuhn‐Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints (MPCC). The performance of the method is illustrated through application to two case studies, the second of which considers economically optimal grade transitions in a polymerization process.     

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015     

Optimal design of integrated agricultural water networks

This paper presents a mathematical programming model for the optimal design of water networks in the agriculture. The proposed model is based on a new superstructure that includes all configurations in terms of use, reuse and regeneration of water in a field constituted by a number of croplands. The model also includes the allocation of pipelines, pumps and storage tanks in different irrigation periods. The objective function consists in maximizing the annual profit that is formed by the economic incomes owing to the crop sell minus the costs for fresh water, fertilizer, storage tanks, treatment units, piping and pumping. The proposed multi-period optimization problem is formulated as a mixed integer non-linear programming formulation, which was applied to a case study to demonstrate the economic, environmental and social benefits that can be obtained.     

Optimization of grade transitions in polyethylene solution polymerization process under uncertainty

Model-based dynamic optimization is an effective tool for control and optimization of chemical processes, especially during transitions in operation. This study considers the dynamic optimization of grade transitions for a solution polymerization process. Here, a detailed dynamic model comprises the entire flowsheet and includes a method-of-moments reactor model to determine product properties, a simple yet accurate vapor–liquid equilibrium (VLE) model derived from rigorous calculations, and a variable time delay model for recycle streams. To solve the grade transition problem, both single stage and multistage optimization formulations have been developed to deal with specification bands of product properties.This dynamic optimization framework demonstrates significant performance improvements for grade transition problems. However, performance can deteriorate in the presence of uncertainties, disturbances and model mismatch. To deal with these uncertainties, this study applies robust optimization formulations through the incorporation of back-off constraints within the optimization problem. With back-off terms calculated from Monte Carlo simulations, the resulting robust optimization formulation can be solved with the same effort as the nominal dynamic optimization problem, and the resulting solution is shown to be robust under various uncertainty levels with minimal performance loss. Additional case studies show that our optimization approach extends naturally to different regularizations and multiple sources of uncertainty.     

Multiobjective optimization of multiproduct batch plants scheduling under environmental and economic concerns

In batch process scheduling, production trade‐offs arise from the simultaneous consideration of different objectives. Economic goals are expressed in terms of plant profitability and productivity, whereas the environmental objectives are evaluated by means of metrics originated from the use of life cycle assessment methodology. This work illustrates a novel approach for decision making by using multiobjective optimization. In addition, different metrics are proposed to select a possible compromise based on the distance to a nonexistent utopian solution, whose objective function values are all optimal. Thus, this work provides a deeper insight into the influence of the metrics selection for both environmental and economic issues while considering the trade‐offs of adopting a particular schedule. The use of this approach is illustrated through its application to a case study related to a multiproduct acrylic fiber production plant, special attention is put to the influence of product changeovers. © 2010 American Institute of Chemical Engineers AIChE J, 57: 2766–2782, 2010     

Process Analysis and Multi‐Objective Optimization of Ionic Liquid‐Containing Acetonitrile Process to Produce 1,3‐Butadiene

Using ionic liquid (IL) [C₂MIM][PF₆] as an additive could remarkably improve the performance of the acetonitrile (CAN) process, which is the most widely used distillation process to produce 1,3‐butadiene (1,3‐BT). In this work, a rigorous simulation of a new IL process to produce 1,3‐BT was carried out to evaluate the performance of IL additive on an industrial scale, using UNIFAC as the global thermodynamic model. Based on the simulation models, some key operation parameters, such as solvent ratio and reflux ratio, were determined by sensitivity analysis. Furthermore, a multi‐objective optimization was proposed and performed considering both the energy consumption and environmental impact (green degree) of the new process. A nonlinear mathematical model was established to express this multi‐objective optimization problem, which includes six decision variables and involves maximizing the green degree of the process, the purity and the recovery of 1,3‐BT, and minimizing the energy consumption of the process. The optimization results showed that the energy consumption of the IL‐containing process could be reduced by 22 % and that its green degree could be improved by 9.2 %.     

Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances

Traditionally the design of supply chains has been based on economic objectives. However, as societal environment concerns grows, environmental aspects are also emerging at academic and industry levels as decisive factors within the supply chain management context. The investment towards logistics structures that considers both economic and environmental performances is nowadays an important and current research topic.This paper addresses the planning and design of supply chain structures for annual profit maximization, while considering environmental aspects. The latter are accounted for through the Eco-indicator methodology. Profit and environmental impacts are balanced using an optimization approach adapted from symmetric fuzzy linear programming (SFLP), while the supply chain is modelled as a mixed integer linear programming (MILP) optimization problem using the Resource-Task-Network (RTN) methodology. The obtained model applicability is validated through the solution of a set of supply chain problems.     

Optimal Design and Economic Evaluation of Dividing‐Wall Columns

In an effort to reduce costs, a systematic optimization approach is proposed to address the energy consumption of dividing‐wall columns (DWCs). This iterative optimization procedure begins by minimizing the overall heat duty using an innovative objective function within a constrained design space. A sensitivity analysis is then carried out on the manipulated variables to obtain their optimal ranges. Optimal operating parameters are obtained through the evaluation of the total annualized cost (TAC). For the separation process of benzene/toluene/o‐xylene, the optimal DWC flow sheet exhibits a significant decrease in TAC when compared to conventional flow sheet optimum designs. The applied optimization method and sensitivity analysis have proven to produce results at the global optimum. This method is both practical and easily applied to other systems, even to systems with more than three components.     

Heat exchanger network synthesis with detailed exchanger designs: Part 1. A discretized differential algebraic equation model for shell and tube heat exchanger design

A new method for the detailed design of shell and tube heat exchangers is presented through the formulation of coupled differential heat equations, along with algebraic equations for design variables. Heat exchanger design components (tube passes, baffles, and shells) are used to discretize the differential equations and are solved simultaneously with the algebraic design equations. The coupled differential algebraic equation (DAE) system is suitable for numerical optimization as it replaces the nonsmooth log mean temperature difference (LMTD) term. Discrete decisions regarding the number of shells, fluid allocation, tube sizes, and number of baffles are determined by solving an LMTD-based method iteratively. The resulting heat exchanger topology is then used to discretize the detailed DAE model, which is solved as a nonlinear programming model to obtain the detailed exchanger design by minimizing an economic objective function through varying the tube length. The DAE model also provides the stream temperature profiles inside the exchanger simultaneously with the detailed design. It is observed that the DAE model results are almost equal to the LMTD-based design model for one-shell heat exchangers with constant stream properties but shows significant differences when streams properties are allowed to vary with temperature or the number of shells are increased. The accuracy of the solutions and the required computational costs show that the model is well suited for solving heat exchanger network synthesis problems combined with detailed exchanger designs, which is demonstrated in Part 2 of the paper.     

A property-based optimization of direct recycle networks and wastewater treatment processes

This article presents a mathematical programming approach to optimize direct recycle-reuse networks together with wastewater treatment processes in order to satisfy a given set of environmental regulations. A disjunctive programming formulation is developed to optimize the recycle/reuse of process streams to units and the performance of wastewater treatment units. In addition to composition-based constraints, the formulation also incorporates in-plant property constraints as well as properties impacting the environment toxicity, ThOD, pH, color, and odor. The MINLP model is used to minimize the total annual cost of the system, which includes the cost for the fresh sources, the piping cost for the process integration and the waste stream treatment cost. An example problem is used to show the application of the proposed model. The results show that the simultaneous optimization of a recycle network and waste treatment process yields significant savings with respect to a commonly-used sequential optimization strategy. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

化工过程设计的多目标不确定优化研究

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.     

Modeling,optimization, and cost analysis of an IGCC plant with a membrane reactor for carbon capture

This article presents a theoretical study on the integration of a membrane reactor (MR) for carbon capture into an integrated gasification combined cycle (IGCC) plant. First‐principles, simplified systems‐level models for the individual IGCC units and the MR are introduced for their subsequent plantwide integration. The integrated plant model is then used for simulation studies that assume different MR characteristics. Using this model, an optimization problem is formulated to analyze the MR effects when adding it to the IGCC plant, while satisfying all of the process constraints in streams and performance variables. The solution of this optimization problem indicates improvements in the original case studies, including capital cost savings as high as $18 million for the optimal case under nominal process conditions. To determine the cost implications of inserting the MR into the IGCC plant, a differential cost analysis is performed taking into account major plant capital and operating costs. This analysis considers the same amount of coal and power generation for cases with and without the MR. The results of this analysis based on a present value of annuity calculation show break even costs for the MR within the feasible range for membrane fabrication, even for short membrane lifetimes. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1568–1580, 2016     

Global optimization in property‐based interplant water integration

This article presents a new global optimization method for the interplant water integration based on properties to characterize streams with numerous components. The problem is formulated as an mixed‐integer non‐linear programming (MINLP) model based on a superstructure that involves all possible options of interest (i.e., reuse and recycle in the same and to other plants and a set of shared treatment units). This formulation exhibits multiple local minima, and to overcome this problem, this article proposes effective branching rules in addition to two new reformulations for the upper bound (integer feasible solution) and the lower limit (relaxed solution), which are incorporated into a spatial branch and bound procedure to handle the bilinear terms in the model. The objective consists in finding the configuration with the minimum total annual cost. Results show that the global optimal solution (involving significant reductions in the fresh water consumption) is reached in few iterations and short central processing unit (CPU) time. © 2012 American Institute of Chemical Engineers AIChE J, 59: 813–833, 2013     

能量集成常减压系统的简捷模型及优化

针对常减压装置,提出基于Fenske-Underwood-Gilliland方程和焓衡算的简捷模型对现有的常减压系统复杂塔进行操作型计算和优化,并利用夹点分析确定系统的热回收目标。以常减压系统的年净利润最大为目标,同时考虑系统的产品收益和能量集成,建立了基于PSO随机粒子群算法的优化方法。以现有的常减压装置为算例,得到了年净利润最大条件下的各工艺参数。通过与严格模拟对比,表明常减压系统简捷模型能够得到较准确的计算结果。     

Computer‐Aided Molecular Design of Environmentally Friendly Solvents for Separation Processes

This review paper presents an optimization technology for the computer‐aided molecular design of environmentally friendly solvents. The approach combines a stochastic optimization method and group contribution methods (GC‐methods) to design solvents with desirable physicochemical and environmental properties. A simulated annealing algorithm is used to investigate feasible molecular structures. The main objective method is adopted to balance the multi‐objective functions. One property is chosen as the main objective function, while the other properties are considered as constraints, and thus, the multi‐objective problem is transformed into a single objective one. The optimal solution is a set of molecules satisfying the formulated target. The properties of each molecule are evaluated through GC‐methods, including pure component properties, mixture properties and environmentally properties. Finally, the proposed methodology is illustrated with several examples of industrial separations.