Model‐predictive safety system for proactive detection of operation hazards

A method of designing model‐predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time‐horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2024–2042, 2016     

应用群能量恒定粒子群优化算法的批次流加发酵过程批次间优化(英文)

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.     

Multiobjective particles swarm optimization and multicriteria decision making of improved cumene production process including economic,environmental, and safety criteria

Cumene is one of the five chemicals with the highest production in the world. In this work, the design by Flegiel was improved to increase the production rate of the cumene process by adding a trans-alkylation reactor, then multi-objective optimization (MOO) using the particles swarm optimization (PSO) algorithm is used to improve the process design. Furthermore, seven multicriteria decision-making (MCDM) methods for selecting an optimal solution from the Pareto-optimal front related to two MOO problems were performed. In this optimization, conflicting objectives such as total capital cost (TCC), energy cost, wastage rate, and safety target are simultaneously minimized in the format of trade-offs. Finally, the results of this work were compared with those reported designs. The optimal solution chosen by MCDM methods is at TCC = 5589, damage index (DI) = 0.044, and material loss = 0.0005.     

A feedback control framework for safe and economically‐optimal operation of nonlinear processes

Maintaining safe operation of chemical processes and meeting environmental constraints are issues of paramount importance in the area of process systems and control engineering, and are ideally achieved while maximizing economic profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have been directed toward integrating the rather disparate domains of process safety and process control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation accounting directly for process economics considerations. However, there is very limited work on the problem of integrating safety considerations in EMPC to ensure simultaneous safe operation and maximization of process profit. Motivated by the above considerations, this work develops three EMPC schemes that adjust in real‐time the size of the safety sets in which the process state should reside to ensure safe process operation and feedback control of the process state while optimizing economics via time‐varying process operation. Recursive feasibility and closed‐loop stability are established for a sufficiently small EMPC sampling period. The proposed schemes, which effectively integrate feedback control, process economics, and safety considerations, are demonstrated with a chemical process example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2391–2409, 2016     

Maintenance scheduling and process optimization under uncertainty

In this paper, we describe an optimization framework for (i) deriving optimal maintenance policies in continuous process operations in the presence of parametric uncertainty and (ii) analyzing and quantifying the impact of uncertainty on optimal maintenance schedules. A systems effectiveness measure is introduced which depends on expected process profitability and process and reliability/maintenance characteristics. A mixed integer nonlinear optimization model is proposed which aims at identifying the number of maintenance (preventive or corrective) actions required over a given time horizon of interest, the time instants and sequence of these maintenance actions on the various components of the process system, so that the system effectiveness is maximized. By introducing the concept of availability threshold values, it is shown that an efficient solution strategy can be established which requires the solution of much smaller nonlinear optimization problems. The application of the proposed framework to an example problem highlights the important interactions between process operation and maintenance scheduling in the presence of uncertainty.     

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Tight integration through material and energy recycling is essential to the energy efficiency and economic viability of process and energy systems. Equation‐oriented (EO) steady‐state process simulation and optimization are key enablers in the optimal design of integrated processes. A new process modeling and simulation concept based on pseudo‐transient continuation is introduced. An algorithm for reformulating the steady‐state models of process unit operations as differential‐algebraic equation systems that are statically equivalent with the original model is presented. These pseudo‐transient models improve the convergence of EO process flowsheet simulations by expanding the convergence basin. This concept is used to build a library of pseudo‐transient models for common process unit operations, and this modeling concept seamlessly integrates with a previously developed time‐relaxation optimization algorithm. Two design case studies are presented to validate the proposed framework. © 2014 American Institute of Chemical Engineers AIChE J 60: 4104–4123, 2014     

A tribute to professor Roger Sargent: Intellectual leader of process systems engineering

This article and this issue of the AIChE Journal, is a tribute to Professor Roger Sargent who, as pioneer and intellectual leader of process systems engineering, has had a profound impact on the discipline of chemical engineering. Spanning more than five decades, his work has provided a strong mathematical foundation to process systems engineering through the development of sophisticated mathematical and computational tools for the simulation, design, control, operation and optimization of chemical processes. In this article we first give a brief overview of his career that included several leadership positions and the establishment of the Centre for Process Systems Engineering (CPSE) at Imperial College London. We next review his research contributions in the areas of process modeling, differential algebraic systems, process dynamics and control, nonlinear optimization and optimal control, design under uncertainty, and process scheduling. We highlight the tremendous impact that he has had through his students, students' students, and his entire academic family tree, which at present contains over 2000 names, probably one of the largest among the academic leaders of chemical engineering. Finally, we provide a brief overview of him as a modest and charming individual with a wonderful sense of humor. He is without doubt a true intellectual giant who has helped to expand the scope of chemical engineering by providing a strong systems component to it, and by establishing strong multidisciplinary links with other fields. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2951–2958, 2016     

Advanced trust region optimization strategies for glass box/black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J. 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3934–3943, 2018     

多目标粒子群算法用于补料分批生化反应器动态多目标优化

多目标优化是过程系统工程的重要课题，通常以加权或约束方式将其转换为单一目标，未能反映多目标间的复杂关系，不利于随时根据需求作出有效的决策。基于群智能的粒子群算法具有全局优化性能，且易于实现。为使其适于多目标优化，应拓展功能，实施改造。以Pareto支配概念评价种群个体的优劣，设计了确定局部最优点和全局最优点的操作。又利用各粒子的局部最优点信息进行速度更新，以加强种群的多样性，避免因早熟而陷于局部最优。还设置了外部优解库，并通过分散度计算，以适当的策略进行更新，使之逐步均匀地逼近于Pareto最优解集。由此构建一种多目标粒子群优化算法（multi-objective particle swarm optimization，MOPSO），并用于补料分批生化反应器的动态多目标优化，取得了满意的结果。可基于所搜得的Pareto最优解集，分析目标间的关系，为合理决策提供有效的支持。经与NSGA-II比较，MOPSO算法具有更为优良的性能。     

Assessment of inherently safer alternatives in biogas production and upgrading

Biogas is becoming an increasingly important resource of energy production from biomass, and a number of alternative technologies are proposed for its production and upgrading. However, in spite of the increasing number of accidents recorded, scarce attention was dedicated to date to the control and mitigation of biogas hazards. In this study, inherent safety of biogas technologies was addressed. A method for the selection of inherently safer alternatives during early design stages was further developed and combined to a Monte Carlo sensitivity analysis, accounting for uncertainty of input parameters and addressing the robustness of the ranking provided. The method was applied to the assessment of several alternative reference process schemes for biogas production and upgrading. The results allowed the identification of critical safety issues and the ranking of inherently safer solutions. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2713–2727, 2016     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

Distributed economic model predictive control for operational safety of nonlinear processes

Achieving operational safety of chemical processes while operating them in an economically‐optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety‐based constraints within model predictive control (MPC) design; however, the safety‐based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety‐based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov‐based economic model predictive control (LEMPC) formulated with safety‐based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed‐loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3404–3418, 2017     

Using functional programming to recognize named structure in an optimization problem: Application to pooling

Branch‐and‐cut optimization solvers typically apply generic algorithms, e.g., cutting planes or primal heuristics, to expedite performance for many mathematical optimization problems. But solver software receives an input optimization problem as vectors of equations and constraints containing no structural information. This article proposes automatically detecting named special structure using the pattern matching features of functional programming. Specifically, we deduce the industrially‐relevant nonconvex nonlinear Pooling Problem within a mixed‐integer nonlinear optimization problem and show that we can uncover pooling structure in optimization problems which are not pooling problems. Previous work has shown that preprocessing heuristics can find network structures; we show that we can additionally detect nonlinear pooling patterns. Finding named structures allows us to apply, to generic optimization problems, cutting planes or primal heuristics developed for the named structure. To demonstrate the recognition algorithm, we use the recognized structure to apply primal heuristics to a test set of standard pooling problems. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3085–3095, 2016     

Control‐relevant decomposition of process networks via optimization‐based hierarchical clustering

A systematic method is proposed for control‐relevant decomposition of complex process networks. Specifically, hierarchical clustering methods are adopted to identify constituent subnetworks such that the components of each subnetwork are strongly interacting while different subnetworks are loosely coupled. Optimal clustering is determined through the solution of integer optimization problems. The concept of relative degree is used to measure distance between subnetworks and compactness of subnetworks. The application of the proposed method is illustrated using an example process network. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3177–3188, 2016     

基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.     

考虑初始操作温度影响的间歇放热反应系统动态柔性分析

It is shown in this article that by changing the initial operation condition of the batch processes, the dynamic performance of the system can be varied largely, especially for the initial operational temperature of the exothermic reaction. The initial operation condition is often ignored in the designing batch processes for flexibility against disturbances or parameter variations. When the initial condition is not rigid as in the case of a batch reactor, where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the ＂best＂ initial condition to use. Problems for dynamic flexibility analysis of exothermic reaction including initial temperature and process operation can be formulated as dynamic optimization problems. Formulations are derived when the initial conditions are considered or not. When the initial conditions are considered, the initial condition can be transferred into control variables in the first optimal step. The solution of the dynamic optimization is on the basis of Rugge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear process problems, enables the determination of the optimal level. The dynamic performance is improved by the proposed method in the two exothermic reaction examples.     

Optimization of Process Conditions for Styrene Epoxidation Based on the Artificial Intelligence Method

A novel prediction and optimization method based on improved generalized regression neural network (GRNN) and particle swarm optimization (PSO) algorithm is proposed to optimize the process conditions for styrene epoxidation to achieve higher yields. This model was designed to optimize the five input parameters reaction temperature and time as well as catalyst, solvent, and oxidant dosage. The output of the improved GRNN was given to the PSO algorithm to optimize the process conditions. The optimal smoothing parameter σ of GRNN was chosen from the training sample with a minimum cross validation error. Under the five optimized process conditions the maximum yield reached 95.76 %. This innovative model of improved GRNN hybrid PSO algorithm proved to be a useful tool for optimization of process conditions for styrene epoxidation.     

An optimal control-based safety system for cost efficient risk management of chemical processes

The design of conventional safety systems is based on failure likelihood and accident severity, which is normally obtained empirically, leaving the system vulnerable to process nonlinearities. To ensure process safety, control actions are conservative and small deviations from setpoints may lead to shutdown, generating economic losses. In this work, periodic simulations of system behavior against failures is proposed in order to determine the potential risk to which the system is subjected. Depending on this potential, preventive actions can be taken in order to guarantee the system safety and integrity and avoid potential shutdown. These actions are calculated to provoke least possible disturbance in order to reduce impact on product quality, while keeping the process operating. The goal is to increase annual operating time of the plant without compromising safety and product quality. Results show that the proposal is feasible to real time applications and unnecessary shutdowns can be avoided.