Efficient generalized shortcut distillation model with improved accuracy for superstructure-based process synthesis

We propose a general shortcut distillation column model that can address a wide range of types of separations including nonsharp/sloppy splits. The model can automatically identify key components and estimate the energy requirement of a desired separation in the presence, potentially, of components with zero flow rates. To obtain more practical column height and vapor flow rates, we augment the model with constraints inspired by the Fenske equation. We also propose valid constraints to enhance the computational performance of the proposed model. Due to its flexibility and computational efficiency, the proposed model can be readily used for superstructure-based process synthesis.     

A superstructure representation,generation, and modeling framework for chemical process synthesis

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on “minimal” and “feasible” component sets for the generation of simple superstructures containing all feasible embedded processes. Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure‐based process synthesis, allowing efficient model generation and modification. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3199–3214, 2016     

Optimal storage design for a multi-product plant: A non-convex MINLP formulation

We discuss a tank design problem for a multi product plant, in which the optimal cycle time and the optimal campaign size are unknown. A mixed-integer nonlinear programming (MINLP) formulation is presented, where non-convexities are due to the tank investment cost, storage cost, campaign setup cost and variable production rates. The objective of the optimization model is to minimize the sum of the production cost per ton per product produced. A continuous-time mathematical programming formulation is proposed and several extensions are discussed. The model is implemented in GAMS and computational results are reported for the two global MINLP solver BARON and LINDOGlobal as well as several nonlinear solvers available in GAMS.     

Nonlinear robust optimization for process design

A novel robust optimization framework is proposed to address general nonlinear problems in process design. Local linearization is taken with respect to the uncertain parameters around multiple realizations of the uncertainty, and an iterative algorithm is implemented to solve the problem. Furthermore, the proposed methodology can handle different categories of problems according to the complexity of the problems. First, inequality‐only constrained optimization problem as studied in most existing robust optimization methods can be addressed. Second, the proposed framework can deal with problems with equality constraint associated with uncertain parameters. In the final case, we investigate problems with operation variables which can be adjusted according to the realizations of uncertainty. A local affinely adjustable decision rule is adopted for the operation variables (i.e., an affine function of the uncertain parameter). Different applications corresponding to different classes of problems are used to demonstrate the effectiveness of the proposed nonlinear robust optimization framework. © 2017 American Institute of Chemical Engineers AIChE J, 64: 481–494, 2018     

Synergistic optimization framework for the process synthesis and design of biorefineries

The conceptual process design of novel bioprocesses in biorefinery setups is an important task,which remains yet challenging due to several limitations.We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically.In this context,several approaches for superstructure optimization based on different surrogate models can be deployed.By means of a case study,the framework is introduced and validated,and the different superstructure optimization approaches are benchmarked.The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues,there remains a potential issue regarding their validation.The development of appropriate surrogate models,comprising the selection of surrogate type,sampling type,and size for training and cross-validation sets,are essential factors.Regarding this aspect,satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem.Furthermore,the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulationbased optimization to consolidate the process design under uncertainty offer an alternative and promising approach.These findings invite for a critical assessment of surrogatebased optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.     

Integrating superstructure-based design of molecules,processes, and flowsheets

The key to many chemical and energy conversion processes is the choice of the right molecule, for example, used as working fluid. However, the choice of the molecule is inherently coupled to the choice of the right process flowsheet. In this work, we integrate superstructure-based flowsheet design into the design of processes and molecules. The thermodynamic properties of the molecule are modeled by the PC-SAFT equation of state. Computer-aided molecular design enables considering the molecular structure as degree of freedom in the process optimization. To consider the process flowsheet as additional degree of freedom, a superstructure of the process is used. The method results in the optimal molecule, process, and flowsheet. We demonstrate the method for the design of an organic Rankine cycle considering flowsheet options for regeneration, reheating, and turbine bleeding. The presented method provides a user-friendly tool to solve the integrated design problem of processes, molecules, and process flowsheets.     

Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages

Heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The model's effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology.     

混合分离过程的有序设计框架

The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.     

A flexible design framework for process systems under demand-side management

Demand response (DR) has been an appealing strategy for both residential and commercial/industrial customers of electricity due to the increasing penetration of renewable energy resources into the power grid and the deregulation of the energy markets. For industrial DR participants, the management of energy consumption along with the satisfaction of production expectations is generally referred to as demand-side management. Recent research in this area has focused mostly on process scheduling and capacity planning, especially for energy-intensive processes. This article presents a new direction through an optimization-based process design paradigm that allows for the potential reconfiguration of the process flow sheet in real time. Based on a case study of pump network design, a network superstructure is first defined, followed by a two-stage stochastic optimization model. The method of progressive hedging is used to solve the resulting mixed-integer stochastic programming problem. The results demonstrate that, under various scenarios, the reconfigurable design offers significant benefits compared to a fixed network structure in terms of total design cost and expected operating cost.     

Simultaneous process and control system design for grade transition in styrene polymerization

We present a comprehensive approach to the simultaneous design and control of a continuous stirred tank reactor (CSTR) for styrene solution polymerization that must be able to produce different polymer grades. The resulting tool allows simultaneous selection of the polymerization equipment, the multivariable feedforward-feedback controller's structure and tuning parameters, the steady states and the transition paths between them. For this purpose a multiobjective optimization is implemented to minimize the annualized reactor cost, the operating costs, the production of off-specification polymer and the transition time between steady states. Trade-offs between the sometimes conflicting objectives are dealt with by the optimization. Unlike many previous grade transition studies, steady states are not known a priori. The only known parameters are the target molecular weights to be produced at each steady state. We have analyzed three different scenarios, and propose practical criteria for selecting the most reasonable optimum when the solution is not unique.     

Sustainable process design by the process to planet framework

Sustainable process design (SPD) problems combine a process design problem with life cycle assessment (LCA) to optimize process economics and life cycle environmental impacts. While SPD makes use of recent advances in process systems engineering and optimization, its use of LCA has stagnated. Currently, only process LCA is utilized in SPD, resulting in designs based on incomplete and potentially inaccurate life cycle information. To address these shortcomings, the multiscale process to planet (P2P) modeling framework is applied to formulate and solve the SPD problem. The P2P framework offers a more comprehensive analysis boundary than conventional SPD and greater modeling detail than advanced LCA methodologies. Benefits of applying this framework to SPD are demonstrated with an ethanol process design case study. Results show that current methods shift emissions outside the analysis boundary, while applying the P2P modeling framework results in environmentally superior process designs. Future extensions of the P2P framework are discussed. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3320–3331, 2015     

The FluxMax approach for simultaneous process synthesis and heat integration: Production of hydrogen cyanide

Resource and energy efficiency are essential in process synthesis of chemical plants as they combine economic with ecological benefits. The two main targets of the process synthesis problem—mass and energy flux optimization—are typically split into two steps: single unit optimization and subsequent energy integration preventing the identification of the globally optimal solution. This article presents a single-step procedure for resource-efficient process synthesis through simultaneous heat and mass flux optimization called FluxMax approach (FMA), which is demonstrated for the production of hydrogen cyanide (HCN). The impact of simultaneous heat integration on the optimal process structure is demonstrated and two resource-optimal processes for HCN production are identified consisting of a combination of different reactor and recycling strategies reducing total variable cost by 68%. For convex objective functions, the globally most resource-efficient process is identified highlighting the potential of the FMA for site planning and retrofitting of existing plants.     

Mathematical modeling for simultaneous design of plants and supply chain in the batch process industry

Most supply chain design models have focused on the integration problem, where links among nodes must be settled in order to allow an efficient operation of the whole system. At this level, all the problem elements are modeled like black boxes, and the optimal solution determines the nodes allocation and their capacity, and links among nodes. In this work, a new approach is proposed where decisions about plant design are simultaneously made with operational and planning decisions on the supply chain. Thus, tradeoffs between the plant structure and the network design are assessed. The model considers unit duplications and the allocation of storage tanks for plant design. Using different sets of discrete sizes for batch units and tanks, a mixed integer linear programming model (MILP) is attained. The proposed formulation is compared with other non-integrated approaches in order to illustrate the advantages of the presented simultaneous approach.     

Membrane system design for multicomponent gas mixtures via mixed-integer nonlinear programming

An optimal design strategy for membrane networks separating multicomponent gas mixtures based on an approximate permeator model and mixed-integer nonlinear programming (MINLP) is proposed. A permeator system superstructure is used to embed a very large number of possible network configurations and allows the permeator feed-side pressure to be fixed or a design variable. A MINLP design model is developed to minimize the total annual process cost by simultaneous optimization of the permeator configuration and operating conditions. Case studies for the separation of acid gases (CO₂ and H₂S) from crude natural gas mixtures with spiral-wound permeators are presented. Permeator configurations are derived for different number of separation stages for both continuous and discrete membrane areas. The method is sufficiently robust to handle product compositions that vary five orders of magnitude. The proposed approach provides an efficient methodology for preliminary screening of multi-stage membrane separation systems for multicomponent gas mixtures.     

A novel network-based continuous-time representation for process scheduling: Part II. General framework

In the first part of this series of papers we presented a new network-based continuous-time representation for the short-term scheduling of batch processes, which overcomes numerous shortcomings of existing approaches. In this second part, we discuss how this representation can be extended to address aspects such as: (i) preventive maintenance activities on unary resources (e.g., processing and storage units) that were planned ahead of time; (ii) resource-constrained changeover activities on processing and shared storage units; (iii) non-instantaneous resource-constrained material transfer activities; (iv) intermediate deliveries of raw materials and shipments of finished products at predefined times; and (v) scenarios where part of the schedule is fixed because it has been programmed in the previous scheduling horizon. The proposed integrated framework can be used to address a wide variety of process scheduling problems, many of which are intractable with existing tools.     

A general framework for process scheduling

Existing methods for process scheduling can be broadly classified as network‐based or sequential. The former are used to address problems where different batches of the same or different tasks are freely mixed or split, whereas the latter are used to address problems where batch mixing/splitting is not allowed. A framework is proposed that allows us to: (1) express scheduling problems in facilities that consist of network and sequential, as well as continuous subsystems, (2) formulate mixed‐integer programming (MIP) scheduling models for such problems, and (3) solve the resulting MIP formulations effectively. The proposed framework bridges the gap between network and sequential approaches, thereby addressing the major formulation challenge in the area of process scheduling, namely, the development of a framework that can be used to address a wide spectrum of problems. © 2010 American Institute of Chemical Engineers AIChE J, 57: 695–710, 2011     

Theoretical framework for formulating MIP scheduling models with multiple and non-uniform discrete-time grids

We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly non-uniform) time grids for each task, unit, and material. Here, we generalize these ideas to account for general resources, and a range of processing characteristics such as limited intermediate storage and changeovers. Each resource has its own grid based on resource consumption and availability allowing resource constraints to be modeled more accurately without increasing the number of binary variables. We develop algorithms to define the unit-, task-, material-, and resource-specific grids directly from problem data. Importantly, we prove that the multi-grid formulation is able to find a schedule with the same optimal objective as the discrete-time single-grid model with an arbitrarily fine grid. The proposed framework leads to the formulation of models with reduced number of binary variables and constraints, which are able to find good solutions faster than existing models.     

基于群体智能算法的换热网络同步最优综合

换热网络同步综合方法一般需要建立复杂的混合整数非线性数学规划模型,该模型具有非凸、非线、不连续的特点,属于最难求解的一类NP-hard问题,应用传统的优化算法很难确定其全局最优解,尤其是对大规模换热网络综合问题,甚至无法在合理时间内接近全局最优的局部最优解。针对换热网络同步综合问题,提出基于群体智能算法的分层优化策略,外层采用离散粒子群算法与遗传算法相结合的混合群体智能算法优化换热网络结构,内层在结构变量给定条件下利用改进粒子群算法优化冷热物流分流比与换热负荷。两个典型算例研究证明了该方法能以较高的效率和稳定性得到较好的优化结果。     

A methodology for simultaneous process and product design in the formulated consumer products industry: The case study of the detergent business

In this work we present a mathematical optimization based methodology for simultaneous formulae and process design in the consumer product business applied to the case of the laundry detergent. The design of a new detergent is formulated as a modified pooling problem including process, performance, processability and environmental constraints. This new features add a number of nonlinearities related to the modeling of the different aspects of the process and customer acceptance. The problem becomes a multiobjective optimization problem that is solved using the ?-constraint method with global optimization techniques to minimize of the environmental impact while minimizing the production cost for a couple of case studies. As future work, further process, product and legal constraints can be added to make the problem more realistic.