Facility siting and plant layout optimization for chemical process safety

Designing the process layout in a chemical plant is a complex and multidisciplinary task requiring input from experts in fields such as chemical, civil, mechanical, and instrument engineering. Plant layout entails the allocation of a given number of facilities on a given piece of land. Determining the optimal distribution of facilities in a plant requires an optimization calculation, including a variety of distance constraints, one of which is related to process safety. A few approaches have been taken to transform consequence analysis, such as toxic gas dispersion and its mitigation as well as the risks of fire and explosions, into mathematical equations as constraints of an optimization problem. An optimization problem with constraints related to safety is not easy to solve given limitations such as nonlinearity, uncertainty, and ethical difficulties in converting human life to costs for calculation purposes. However, safety concerns have increased to the point that developing this type of approach is necessary. The objective of this study was to review the current methodologies for plant layout optimization and to resolve facility siting issues. Process safety concepts are considered with a view to identifying gaps and issues with current methods in order to develop better methodologies for designing safer layouts.     

用神经网络和随机复合形搜索实施在线优化

提出一种基于神经网络和随机复合形法搜索的在线稳态优化方法，并对某厂气体分馏装置的生产过程的优化问题进行测试，得出优化操作条件。     

空分装置吸附器出口二氧化碳含量超标的原因及改造

分析分子筛吸附器出口空气中二氧化碳含量超标的原因，通过模拟计算．提出将低温甲醇洗的二氧化碳尾气改至从锅炉烟囱排放的改造方案，对化工装置的布置提出建议。     

Optimization of a large scale industrial reactor by genetic algorithms

The present work aims to employ genetic algorithms (GAs) to optimize an industrial chemical process, characterized by being difficult to be optimized by conventional methods. The considered chemical process is the three phase catalytic slurry reactor in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol occurs. In order to describe the dynamic behavior of the multivariable process, a non-linear mathematical model is used. Due to the high dimensionality and non-linearity of the model, a rigorous one, the solution of the optimization problem through conventional algorithms does not always lead to convergence. This fact justifies the use of an evolutionary method, based on the GAs, to deal with this process. In this way, in order to optimize the process, the GA code is coupled with the rigorous model of the reactor. The aim of the optimization through GAs is the searching of the process inputs that maximizes the productivity of 2-methyl-cyclohexanol subject to the environmental constraint of conversion. Many simulations are conducted in order to find the maximization of the objective function without violating the constraint. The results show that the GAs are used successfully in the process optimization. The selection of the most important GA parameters making use of a factorial design approach by fractional factorial design is proposed. A factorial design approach by a central composite design is also proposed in order to determine the best values of the GA parameters that lead to the optimal solution of the optimization problem.     

A binary coding genetic algorithm for multi-purpose process scheduling: A case study

This paper presents a novel genetic algorithm (GA) for the scheduling of a typical multi-purpose batch plant with a network structure. Multi-purpose process scheduling is more difficult to deal with compared to single-stage or multi-stage process scheduling. A large amount of literature on this problem has been published and nearly all of the authors used mathematical programming (MP) methods for solution. In the MP methods, a huge number of binary variables, as well as numerous constraints to consider mass balance and sequencing of batches in space/time dimensions, are needed for the large-size problem, which leads to very long computational time. In the proposed GA, only a small part of the binary variables are selected to code into binary chromosomes, which is realized through the identification of crucial products/tasks/units. Due to the logical heuristics utilized to decode a chromosome into a schedule, only the feasible solution space is searched. Our genetic algorithm has first been devised with particular crossover for makespan minimization and then adjusted for production maximization.     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

Generation of operating procedures for a mixing tank with a micro genetic algorithm

This paper explores the use of a micro genetic algorithm that uses variable-length chromosomes and a seeding scheme based on tabu search. The problem is to find the sequence of actions that have to be executed in the shortest time possible, but also in a way that minimizes the possibility of situations that may endanger the plant personnel and plant facilities. The proposed approach was tested on the generation of the optimum sequences for startup and shutdown of a mixing vessel similar to the equipment used in the synthesis of acrylic acid. The results show that the proposed method outperforms the traditional GA algorithm both in terms of the quality of the solution and computational effort.     

Development of an optimal multifloor layout model for the generic liquefied natural gas liquefaction process

Liquefied natural gas (LNG) is attracting significant interest as a clean energy alternative to other fossil fuels, mainly for its ease of transport and low carbon dioxide emission. As worldwide demand for LNG consumption has increased, liquefied natural gas floating, production, storage, and offloading (LNG-FPSO) operations have been studied for offshore applications. In particular, the LNG-FPSO topside process systems are located in limited areas. Therefore, the process plant layout of the LNG-FPSO topside systems will be optimized to reduce the area cost occupied by the topside equipment, and this process plant layout will be designed as a multifloor concept. We describe an optimal layout for a generic offshore LNG liquefaction process operated by the dual mixed refrigerant (DMR) cycle. To optimize the multifloor layout for the DMR liquefaction cycle process, an optimization was performed by dividing it into first and the second cycles. A mathematical model of the multifloor layout problem based on these two cycles was formulated, and an optimal multifloor layout was determined by mixed integer linear programming. The mathematical model of the first cycle consists of 725 continuous variables, 198 equality constraints, and 1,107 inequality constraints. The mathematical model of the second cycle consists of 1,291 continuous variables, 286 equality constraints, and 2,327 inequality constraints. The minimization of the total layout cost was defined as an objective function. The proposed model was applied to DMR liquefaction cycle process to determine the optimal multifloor layout.     

A stochastic programming approach for gas detector placement using CFD-based dispersion simulations

A stochastic programming formulation is developed for determining the optimal placement of gas detectors in petrochemical facilities. FLACS, a rigorous gas dispersion package, is used to generate hundreds of scenarios with different leak locations and weather conditions. Three problem formulations are investigated: minimization of expected detection time, minimization of expected detection time including a coverage constraint, and a placement based on coverage alone. The extensive forms of these optimization problems are written in Pyomo and solved using CPLEX. A sampling procedure is used to find confidence intervals on the optimality gap and quantify the effectiveness of detector placements on alternate subsamples of scenarios. Results show that the additional coverage constraint significantly improves performance on alternate subsamples. Furthermore, both optimization-based approaches dramatically outperform the coverage-only approach, making a strong case for the use of rigorous dispersion simulation coupled with stochastic programming to improve the effectiveness of these safety systems.     

Optimal design of a natural gas transmission network layout

Optimal design of a natural gas network, which is supposed to convey natural gas from a supply point in south of Iran to some delivery points in north and northwest of Iran, is presented in this paper. Sum of investment and operating costs constitutes the objective function of the present study. A wide range of design parameters, including the network layout, diameter of each pipeline, pressure value at each supply or delivery node, as well as number and locations of compressor stations (CSs) on each pipeline, were considered in the optimization problem. A Genetic Algorithm (GA) which exploits “optimal properties of single pipelines” was presented and used as the optimization tool. Short computation time and repeatability of results ensures achieving the global optimum solution and are positive features of the proposed optimization algorithm. The optimal network design obtained from the optimization procedure consisted of 2660 km of pipelines and 26 CSs. It required a total annual cost of about 366.15 M$/year. The results explain why the layout with the shortest total length is not the optimal choice.     

基于遗传算法的快速X射线计算机层析成像多相流测试技术(Ⅰ)测试原理和数值仿真

针对局部浓度场具有二值分布特征的特定多相流体系，提出引入多相流物理特征的快速X射线计算机层析成像（XCT）技术，基于围绕被测物有限角度的X射线投影数据，建立了改进的遗传算法（GA）求解不完整投影数据集的二值图像重构算法。通过有限角度的同时投影，实现对两相流浓度场的瞬间图像冻结和快速图像采集，既拥有XCT的高空间分辨率特征，同时满足高时间分辨率的动态测量要求，并由于投影角度数要求的大幅度缩减而降低了XCT的硬件成本。以气液两相流的多泡体系为研究对象，通过详细的数值仿真实验获得了较理想的图像重构效果，验证了GA-XCT的理论可行性。在3～24个有限角度投影数据的情况下，GA-XCT表现出明显优于传统CT图像重构算法（即滤波反投影算法）的二值图像重构能力。同时，GA-XCT具有很好的抗噪声能力，而且不依赖于气泡形状。     

Valid Inequalities Based on Demand Propagation for Chemical Production Scheduling MIP Models

Although several mixed‐integer programming (MIP) models have been proposed for the scheduling of chemical manufacturing facilities, the development of solution methods for these formulations has received limited attention. In this article, we develop a constraint propagation algorithm for the calculation of lower bounds on the number and size of tasks necessary to satisfy given demand. These bounds are then used to express four types of valid inequalities which greatly enhance the computational performance of the MIP scheduling model. Specifically, the addition of these inequalities leads to reductions in the computational requirements of more than three orders of magnitude, thereby allowing us to address medium‐sized problems of industrial relevance. Importantly, the proposed methods are applicable to a wide range of problem classes and time‐indexed MIP models for chemical production scheduling. © 2013 American Institute of Chemical Engineers AIChE J, 59:872‐887, 2013     

H_2S制硫酸装置外部安全防护距离设计

以某酸性气制硫酸装置酸性气缓冲罐为例,进行外部安全防护距离设计,结合当地多种气象,使用PHAST软件和LEAK软件进行H_2S气体扩散和爆炸计算,扩散结果应用于周边人员集中场所的防毒设计以及企业的应急救援,H_2S爆炸计算结果应用于周边人员集中场所建构筑物的抗爆设计,并和《石油化工工厂布置设计规范》(GB 50984-2014)中的建议值进行比对。     

Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem

This paper introduces a new constraint handling scheme developed for the differential evolutionary algorithm to solve constrained optimization problems. The developed approach uses a repair algorithm based on the gradient information derived from the equality constraint set to correct infeasible solutions. A dominance-based selection scheme is also applied to incorporate constraints into the objective function. To illustrate the developed algorithm and to compare its efficiency with other tradition method, several test problems and chemical engineering optimization problems are used. A traditional constraint handling technique is compared; both in terms of solution quality and the number of function evaluations required. The performance of our developed scheme compares favorably with traditional penalty function method. Our developed algorithm can effectively handle constraints encountered in chemical engineering optimization problems.     

Geometrical Optimization of the Overlap in Mixed Adhesive Lap Joints

The aim of this research is to optimize the geometry of the overlap in mixed adhesive single- and double-lap joints using a modified version of Bees and Genetic Algorithms (BA and GA). Accounting for adherends Poisson's ratio in the deduced equilibrium equations, the proposed shear lag model gives a more accurate approximation of joint failure load in comparison with Volkersen's solution. The objective functions used in this work are used separately to maximize the load bearing capacity f and the specific strength (f/w) of the joint. This procedure is applied to optimize aeronautical adhesively bonded assemblies, while taking manufacturing constraints into account. The employed constraints are the application of yield criterion on adherends as well as geometrical constraint on the overlap length. The proposed straightforward procedure provides 18 optimal configurations amid a wide range of changes for optimization variables, among which the designer can take a choice, depending on his/her goal. The efficiency of the two employed algorithms, BA and GA, in searching for the optimum geometrical design of the mixed adhesive joints have also been investigated. The results show the more robust and efficient performance of the modified version of BA over GA in such kinds of engineering problems.     

A reformulated nonlinear model to solve the facility layout problem

The facility layout problem is fundamental during the plant design. The solution of this problem has required strategies that become in numerical challenges. One of them is the exact method, which finds optimal distributions for facility layout, as demonstrated in the linear and disjunctive models. Both models are distinguished by a high quantity of nonoverlapping constraints. In contrast, a nonlinear model was formulated with low number of constraints, but it has been solved by genetic algorithms. Herein, that model is reformulated to a MINLP (mixed integer nonlinear programing) problem. The solution was possible using a visual interface linked to GAMS (General Algebraic Modeling System). Three case studies were solved to compare them. The reformulated model reached feasible solutions, and its computational time and binary variables were lower than those of the others. This suggests that the reformulated model can be extended to solve topics about process safety.     

Process intensification using energy-free highly enriched air: Application to seawater desalination plants

A new process layout to intensify and make more sustainable chemical processes that use open-loop cooling/heating systems is broached. This new process demonstrates the possibility to recover a stream of highly enriched air without any additional operational cost, but simply exploiting differently the existing energy sources. Enriched air can therefore be used to reduce the variable costs of the intensified plants as well as to reduce the volume of process facilities dedicated to steam generation or to directly increase the net operating margin as additional product (industrial or medical gas). General and dedicated process simulations combined with experimental evidence demonstrate the feasibility and the effectiveness of the proposed intensification. The application to the multi-effect distillation technology for seawater desalination is described.     

A risk-based methodology for the optimal placement of hazardous gas detectors

Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and a majority of process installations have a large number of sensitive detectors in place, the actual operating performance of gas detection systems still does not meet the expected requirements. In this paper, a risk-based methodology is proposed to optimize the placement of hazardous gas detectors. The methodology includes three main steps, namely, the establishment of representative leak scenarios, computational fluid dynamics (CFD)-based gas dispersion modeling, and the establishment of an optimized solution. Based on the combination of gas leak probability and joint distribution probability of wind velocity and wind direction, a quantitative filtering approach is presented to select representative leak scenarios from all potential scenarios. The commercial code ANSYS-FLUENT is used to estimate the consequence of hazardous gas dispersions under various leak and environmental conditions. A stochastic mixed-integer linear programming formulation with the objective of minimizing the total leak risk across all representative leak scenarios is proposed, and the greedy dropping heuristic algorithm (GDHA) is used to solve the optimization model. Finally, a practical application of the methodology is performed to validate its effectiveness for the optimal design of a gas detector system in a high-sulfur natural gas purification plant in Chongqing, China. The results show that an appropriate number of gas detectors with optimal cost-effectiveness can be obtained, and the total leak risk across all potential scenarios can be substantially reduced. This methodology provides an effective approach to guide the optimal placement of point-type gas detection systems involved with either single or mixed gas releases.     

一种基于改进核Fisher的故障诊断方法

针对化工过程故障数据呈非线性分布，数据类别复杂，难以进行故障诊断的问题，提出一种改进核Fisher的故障诊断方法。对于原始样本数据投影后，样本出现因类间距离存在很大差异性而导致部分类别样本存在混叠的现象，以及不同类别的边界数据归类模糊问题，给出了统一的解决办法。该方法首先采用改进类间距的方法来改变样本投影空间的分布，使得样本具有较好的投影效果，然后通过定义阈值参数来筛选出边界数据，对于边界数据，采用改进的K近邻（K-NN）算法来分类，对于非边界数据，采用马氏距离来分类。最后在TE过程中进行了仿真验证，结果表明方法在兼顾了故障诊断时间的同时，有效提高了故障诊断精度。     

基于事故后果的化工厂平面布局安全设计

为降低化工厂内灾害事故对周围其他单元的伤害,借鉴目前欧盟各国的土地使用计划(LUP)探索建立基于事故后果的化工厂平面布局安全设计体系。以事故后果理论为基础,建立了厂区平面布局安全设计规划准则的选择集;对各种模式的事故后果量级进行计算分析,确定了厂区平面布局安全设计中需要考虑的事故模式;对平面布局的设计过程进行分析,制定了基于事故后果的化工厂平面布局安全设计框架。