Simulation based approach for optimal scheduling of fuel gas system in refinery

In refineries, fuel gas, which is continuously produced during the production process, is one of the most important energy sources. In this paper, a brief introduction of fuel gas system is given. The main problems concerned by the fuel gas system scheduler, such as the compressor operation strategy and the fuel assignment decision, are presented. A logical modeling method, which is called generalized disjunctive programming (GDP), is introduced to model the operation of compressors and the flow of branching structure pipeline network in the fuel gas system. The main purpose of this method is to make the modeling process more easily and systematically. In order to effectively deal with the flow rate proportion of loop structure pipeline network in the fuel gas system, an iterative procedure based on pipeline network simulation is proposed. Thus, the solution of a complex MINLP formulation is replaced by the sequential MILP problem. The efficiency of this method, which requires very low computational requirements, is illustrated with a case study.     

Inference of chemical reaction networks using mixed integer linear programming

The manual determination of chemical reaction networks (CRN) and reaction rate equations is cumbersome and becomes workload prohibitive for large systems. In this paper, a framework is developed that allows an almost entirely automated recovery of sets of reactions comprising a CRN using experimental data. A global CRN structure is used describing all feasible chemical reactions between chemical species, i.e. a superstructure. Network search within this superstructure using mixed integer linear programming (MILP) is designed to promote sparse connectivity and can integrate known structural properties using linear constraints. The identification procedure is successfully demonstrated using simulated noisy data for linear CRNs comprising two to seven species (modelling networks that can comprise up to forty two reactions) and for batch operation of the nonlinear Van de Vusse reaction. A further case study using real experimental data from a biodiesel reaction is also provided.     

Stochastic mixed integer nonlinear programming using rank filter and ordinal optimization

A rank filter algorithm is developed to cope with the computational‐difficulty in solving stochastic mixed integer nonlinear programming (SMINLP) problems. The proposed approximation method estimates the expected performance values, whose relative rank forms a subset of good solutions with high probability. Suboptimal solutions are obtained by searching the subset using the accurate performances. High‐computational efficiency is achieved, because the accurate performance is limited to a small subset of the search space. Three benchmark problems show that the rank filter algorithm can reduce computational expense by several orders of magnitude without significant loss of precision. The rank filter algorithm presents an efficient approach for solving the large‐scale SMINLP problems that are nonconvex, highly combinatorial, and strongly nonlinear. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling

The aim of this paper is to introduce a methodology to solve a large-scale mixed-integer nonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planning and crude-oil operations scheduling. The proposed approach consists of using Lagrangian decomposition to efficiently integrate both problems. The main advantage of this technique is to solve each problem separately. A new hybrid dual problem is introduced to update the Lagrange multipliers. It uses the classical concepts of cutting planes, subgradient, and boxstep. The proposed approach is compared to a basic sequential approach and to standard MINLP solvers. The results obtained on a case study and a larger refinery problem show that the new Lagrangian decomposition algorithm is more robust than the other approaches and produces better solutions in reasonable times.     

Managing the water chemistry of a CANDU reactor with an expert system

The aim of this project was to capture the expertise of Ontario Hydro in the water chemistry of the heat transport system (HTS) of the CANDU nuclear reactor and transform it into an Expert System prototype. The end product is an Expert System which can realistically diagnose situations and recommend proper courses of action based on the user's water chemistry analysis.     

炼油企业全厂调度优化系统的设计与开发

生产调度优化对于炼油企业提高经济效益、增强市场响应速度有着重要的作用。本文设计并开发出一款图形化炼厂生产调度优化软件,采用MVC架构技术,实现了调度优化建模的图形化,可以方便组态炼油厂调度模型;将开源优化代码Coin-OR移植为调度优化软件的求解器,实现了求解器与图形化建模接口的连接;丰富的报表输出,让现场操作工可以各种形式获得调度优化结果;完善的内部模型数据管理和丰富的外部数据接口,包括原油性质指标库和原油评价数据库接口、成品油性质指标库等。仿真结果表明,该软件能够优化出符合炼厂生产实际的调度优化排产方案。     

Control of pH neutralization process using simulation based dynamic programming

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.     

A knowledge-based expert system for the prediction of ternary azeotrope formation in organic mixtures

New functions of AZEOPERT [Kim and Simmrock, 1997] were investigated to predict the occurrence of ternary azeotropes and their azeotropic compositions in an organic mixture. This study describes its new problem-solving strategy. The knowledge base of AZEOPERT for ternary azeotropes is hierarchically structured with the several levels of domain-specific knowledge on ternary azeotropy. First, an azeotropic data bank including ternary azeotropic experimental data was implemented in AZEOPERT as the lowest level. It may be used to determine whether or not ternary azeotropic experimental data for the consulted organic mixture are already available. Moreover, compiled heuristic knowledge as the second level and class-oriented model-based knowledge as the highest level were implemented in the knowlege base. The problem-solving strategy through the integration of model-based reasoning into compiled reasoning gives a very efficient, general way for the prediction of ternary azeotrope formation in a wide varitey of organic mixtures, and especially, in unknown mixture systems.     

Soft sensors model optimization and application for the refinery real-time prediction of toluene content

Industrial facilities nowadays show an increasing need for continuous measurements, monitoring and controlling many process variables. The on-line process analyzers, being the key indicators of process and product quality, are often unavailable or malfunction. This paper describes development of soft sensor models based on the real plant data that could replace an on-line analyzer when it is unavailable, or to monitor and diagnose an analyzer’s performance. Soft sensors for continuous toluene content estimation based on the real aromatic plant data are developed. The autoregressive model with exogenous inputs, output error, the nonlinear autoregressive model consisted of exogenous inputs and Hammerstein–Wiener models were developed. In case of complex real-plant processes a large number of model regressors and coefficients need to be optimized. To overcome an exhaustive trial-and-error procedure of optimal model regressor order determination, differential evolution optimization method is applied. In general, the proposed approach could be, of interest for the development of dynamic polynomial identification models. The performance of the models are validated on the real-plant data.     

在线化工设备疲劳分析专家系统的研究

运用应力分析、疲劳分析的理论，综合专家学者的知识和经验，针对现代化大型联合企业技术管理的现状，提出了建立在线化工设备及管道安全评定中疲劳分析的专家系统方法，并编制了初级的软件。用简化试验来模拟一条高温高压蒸汽管线的疲劳裂纹扩展，并用之考评本软件。     

Thermodynamic design of a cascade refrigeration system of liquefied natural gas by applying mixed integer non-linear programming

Liquefied natural gas (LNG) is the most economical way of transporting natural gas (NG) over long distances. Liq-uefaction of NG using vapor compression refrigeration system requires high operating and capital cost. Due to lack of systematic design methods for multistage refrigeration cycles, conventional approaches to determine op-timal cycle are largely trial-and-error. In this paper a novel mixed integer non-linear programming (MINLP) model is introduced to select optimal synthesis of refrigeration systems to reduce both operating and capital costs of an LNG plant. Better conceptual understanding of design improvement is illustrated on composite curve (CC) and exergetic grand composite curve (EGCC) of pinch analysis diagrams. In this method a superstruc-ture representation of complex refrigeration system is developed to select and optimize key decision variables in refrigeration cycles (i.e. partition temperature, compression configuration, refrigeration features, refrigerant flow rate and economic trade-off). Based on this method a program (LNG-Pro) is developed which integrates VBA, Refprop and Excel MINLP Solver to automate the methodology. Design procedure is applied on a sample LNG plant to illustrate advantages of using this method which shows a 3.3% reduction in total shaft work consumption.     

Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming

Hybrid systems are dynamical systems characterized by the simultaneous presence of discrete and continuous variables. Model‐based control of such systems is computationally demanding. To this effect, explicit controllers which provide control inputs as a set of functions of the state variables have been derived, using multiparametric programming mainly for the linear systems. Hybrid polynomial systems are considered resulting in a Mixed Integer Polynomial Programming problem. Treating the initial state of the system as a set of bounded parameters, the problem is reformulated as a multiparametric Mixed Integer Polynomial optimization (mp‐MIPOPT) problem. A novel algorithm for mp‐MIPOPT problems is proposed and the exact explicit control law for polynomial hybrid systems is computed. The key idea is the computation of the analytical solution of the optimality conditions while the binary variables are treated as relaxed parameters. Finally, using symbolic calculations exact nonconvex critical regions are computed. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3441–3460, 2016     

专家系统在化工中的应用进展

本文综述了近年来专家系统在化工领域的应用状况，并指出今后的发展趋势。     

A fuzzy expert system application to the evaluation of ceramic- and paper-quality kaolin

This paper describes the results obtained from the application of a fuzzy expert system to the zonification of a deposit in terms of kaolin of a quality suitable for the paper and ceramic industries. In the first stage of the study, mineralogical, chemical and paper quality indices were characterized for samples taken from a kaolin deposit located in Galicia in NW Spain. In the second part of the study the expert system was trained and adjusted on the basis of analytical data, and its reliability in classifying samples in different quality groups was tested.Data analysis and the design and training of a fuzzy-logic expert system furnish the tool not only with general information inherent to kaolin mining but also with an input in the form of the local characteristics of the studied deposit. This method enables mining experts to identify the areas of a deposit containing kaolin that meets the criteria necessary for enrichment and subsequent use in the ceramic or paper industry. This kind of tool is not only indispensable in planning extraction tasks, but also permits technical and financial risk to be minimized. The model is innovative in that it automatically classified kaolin quality in terms of saleability, thereby emulating the logical reasoning processes of the human expert.     

通用炼油厂水系统优化模型开发与应用

传统水系统优化往往只考虑了新鲜水用量,而忽视了除盐水、除氧水、各等级蒸汽、蒸汽冷凝水、循环冷却水等类型的水,缺乏对各类型水量关联性的研究。针对这种局限性,提出了包含多种类型水的通用用水过程模型,以及通用炼油厂水系统优化的超结构和相应的数学模型,模型中集成了各装置及各种类型水之间关联的物料衡算方程。利用商业软件GAMS对某炼油厂的水系统进行建模求解,案例分析结果表明,除盐水用量有所下降,回用水用量增加,在优化前后雨水量保持469.36 t·h^-1的情况下,系统的新鲜水用量从489.44 t·h^-1减小到283.94 t·h^-1,以系统新鲜水量和雨水量之和为基准,节水率达到21.4%。加工吨原油取水量从0.649 t减小至0.510 t,接近国内先进水平。案例研究表明,提出的优化模型能够有效地计算整个炼油厂水系统的新鲜水用量,验证了该模型的实用性。     

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

Scheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution.     

Development of a batch manager for dynamic scheduling and process management in multiproduct batch processes

A batch manager is developed for the dynamic scheduling and on-line management of process operations. The developed system consists of a process monitoring module and a dynamic scheduling module. When a deviation from the initial schedule is detected in a process monitoring module, dynamic scheduling is performed in the dynamic scheduling module and the initial schedule is adjusted to the proper schedule by using rescheduling algorithms presented in this paper. The adjusted schedule is shown in the process monitoring module. The dynamic scheduler in the batch manager copes with several unexpected process events of batch process operations by adjusting the EST (Earliest Start Time) of equipment, redetermining the batch path and reassigning tasks to equipment. This study focuses on the implementation of a batch manager with on-line dynamic scheduling for batch process management. Examples of fodder production batch processes illustrate the efficiency of the algorithms. This paper was supported by nondirected research fund, Korea Research Foundation, 1997.     

高凝高粘油井机采系统效率影响因素分析

本文分析了大港油田高凝高粘油井系统效率现状,双高油井系统效率偏低,而且井下效率远小于地面效率,井下效率提高潜力较大。影响双高油井系统效率的因素包括冲程、冲次、泵效、沉没度、悬点交变载荷等参数。将采取配套措施的高凝高粘油井的加热参数、掺水等参数纳入到优化设计计算中,提出了兼顾经济效益并以最大系统效率为目标进行系统优化方法,增强了以提高系统效率为目的的优化设计方法对双高油井的适应性,通过对优化设计结果进行分析得出高凝高粘油井系统效率最高的优化设计方案并非最优方案的结论,对提高采取配套措施的高凝点、高粘度油井的机采系统效率具有指导意义。     

Global optimization of large‐scale mixed‐integer linear fractional programming problems: A reformulation‐linearization method and process scheduling applications

Mixed‐integer linear fractional program (MILFP) is a class of mixed‐integer nonlinear programs (MINLP) where the objective function is the ratio of two linear functions and all constraints are linear. Global optimization of large‐scale MILFPs can be computationally intractable due to the presence of discrete variables and the pseudoconvex/pseudoconcave objective function. We propose a novel and efficient reformulation–linearization method, which integrates Charnes–Cooper transformation and Glover's linearization scheme, to transform general MILFPs into their equivalent mixed‐integer linear programs (MILP), allowing MILFPs to be globally optimized effectively with MILP methods. Extensive computational studies are performed to demonstrate the efficiency of this method. To illustrate its applications, we consider two batch scheduling problems, which are modeled as MILFPs based on the continuous‐time formulations. Computational results show that the proposed approach requires significantly shorter CPU times than various general‐purpose MINLP methods and shows similar performance than the tailored parametric algorithm for solving large‐scale MILFP problems. Specifically, it performs with respect to the CPU time roughly a half of the parametric algorithm for the scheduling applications. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4255–4272, 2013     

Treatment of mixed solvent vapors with hybrid system composed of biofilter and photo-catalytic reactor

The transient behavior of a hybrid system composed of biofilter and photo-catalytic reactor was observed at the height of each sampling port to treat waste-air containing ethanol. The biofilter packed with mixed media (of granular activated carbon and compost) was inoculated with a pure culture ofBurkholderia cepacia G4 andPseudomonas putida, while a photo-catalytic reactor was composed of 15W UV-A lamps and annular pyrex tubes packed with glass beads coated with sol type of TiO₂ before calcination. The maximum elimination capacities of toluene and ethanol turned out to be 130 g/m³/h and 230 g/m³/h, respectively, which were greater by 40 g/m³/h and 130 g/m³/h, respectively, than those from the experiments performed with a biofilter only. Thus, the maximum elimination capacities for toluene and ethanol increased by 44% and 130%, respectively, by use of a hybrid system. The photo-catalytic process contributed to the maximum elimination capacities of hybrid system on toluene and ethanol by 30.8% and 56.5%, respectively, which contributions for the elimination capacities on toluene and ethanol were allocated indirectly by 25.4% and 44.3% as well as directly by 5.4% and 12.2%, respectively. Direct contributions of photo-catalytic process were 17.5% and 21.5% to the increments of the elimination capacities on toluene and ethanol, respectively, while its indirect contributions were 82.5% and 78.5% to those on toluene and ethanol, respectively.