Logarithm-transform piecewise linearization method for the optimization of fasoline blending processes

Gasoline blending is a key process in a petroleum refinery, as it can yield 60%–70% of a typical refinery's total revenue. This process not only exhibits non-convex nonlinear blending behavior due to the complicated blend mechanism of various component feedstocks with different quality properties, but also involves global optimum searching among numerous blending recipes. Since blend products are required to meet a series of quality requirements and highly-sensitive to the proportion changes of blending feedstocks, global optimization methods for NLP problems are often difficult to be applied because of heavy computational burdens. Thus, piecewise linearization methods are naturally proposed to provide an approximate global optimum solution by adding binary variables into the models and converting the original NLP problems into MILP ones. In this paper, Logarithmtransform piecewise linearization(LTPL) method, an improved piecewise linearization, is proposed. In this method a logarithm transform is applied to convert multi-variable multi-degree constraints into a series of single-variable constraints. As a result, the number of 0–1 variables is greatly reduced. In the final part of this paper, an industrial case study is conducted to demonstrate the effectiveness of LTPL method. In principle, this method would be useful for blending problems with complicated empirical or theoretical models.     

Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes☆

Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in-cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal com-ponent analysis. In this approach, mutual information estimation and Bayesian information criterion (BIC) are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation (BP) network is applied whose input is multivar-iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis (LKPCA) model for incipient fault prognosis. The new method has been exemplified in a sim-ple nonlinear process and the complicated Tennessee Eastman (TE) benchmark process. The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods. ? 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. Al rights reserved.     

ON THE MINLP APPROACH FOR OPTIMAL DESIGN OF CHEMICAL ENGINEERING SYSTEMS (I) A GLOBAL OPTIMIZATION METHOD FOR NONCONVEX MINLP PROBLEMS

An algorithm for global optimization of a class of nonconvex MINLP problems is devel-oped and presented in this paper.By partitioning the variables,dual representation of the primal ofsubproblems and outer-approximation strategy are used to develop a representative relaxed iterativeproblem.Then the original MINLP problem is replaced by a series of subproblems and relaxediterative problems.By exploiting the particular form of the nonconvex MINLP problem,the feasibleregion of this problem is explicitly included in the representative problem,thus the inconvenienceencountered with the GBD method can be avoided.The proposed method is illustrated andinterpreted geometrically with an example problem.     

基于混合微分进化算法的生化过程动态参数估计(英文)

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.     

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier☆

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP (Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm (DE), genetic algorithm (GA), and parti-cle swarm optimization algorithm (PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO (3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algo-rithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effective-ly. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms (EAs) can be improved, and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coal-water slurry gasifier shows outstanding computing results than actual industry use and other algorithms.     

流化床化学气相沉积多晶硅的技术进步与挑战

Various methods for production of polysilicon have been proposed for lowering the production cost and energy consumption, and enhancing productivity, which are critical for industrial applications. The fluidized bed chemical vapor deposition (FBCVD) method is a most promising alternative to conventional ones, but the homogeneous reaction of silane in FBCVD results in unwanted formation of fines, which will affect the product quality and output. There are some other problems, such as heating degeneration due to undesired polysilicon deposition on the walls of the reactor and the heater. This article mainly reviews the technological develop-ment on FBCVD of polycrystalline silicon and the research status for solving the above problems. It also identifies a number of chal-lenges to tackle and principles should be followed in the design of a FBCVD reactor.     

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is in-troduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to re-trieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.     

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.     

电容层析成像技术在分离器料腿浓度测量中的应用

Accurate solid concentration measurement plays a key role in the process industry. Measurements analyzed offline can be used to estimate process efficiencies, to identify problems in a flow, and to validate computational models. Online measurements can be used for active control. Electrical capacitance tomography (ECT) is a unique measuring technique with great potential in multiphase flow measurement. Experimental studies are carried out on a solid concentration measurement in a cyclone separator dipleg, using ECT. In this experiment eight electrodes are selected for the ECT sensor that is placed on the straight tube of the dipleg. The fluctuating characteristics according to the screw feeder and the effect of the airflow rate from the top of the cyclone are analyzed. The feasibility and reliability of the method are verified by the experimental results.     

An improved control vector iteration approach for nonlinear dynamic optimization. II. Problems with path constraints简

This paper considers dealing with path constraints in the framework of the improved control vector iteration （CVI） approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.     

大规模非线性优化软件包UniOptima的设计和开发

根据将SQP算法应用于大规模问题求解的两种改造思路,并结合目前这方面的研究成果,在M atlab环境下开发了大规模非线性优化工具箱Un iOptim a。Un iOptim a包括两个非线性求解器RSQP和THSQP,RSQP专门应用于自由度相对较小的等式约束优化问题,THSQP则应用于一般的非线性优化问题。本优化工具箱采用M atlab语言开发,并与M atlab优化工具箱保持了良好的兼容性,大大方便了用户使用。求解器中设有很多优化选项,用户可根据不同情况或不同需要选择不同的优化选项,增强了用户和程序的交互性。     

Chance constrained optimization of process systems under uncertainty: I. Strict monotonicity

An approach for chance constrained programming of large-scale nonlinear dynamic systems is presented. The stochastic property of the uncertainties is explicitly considered in the problem formulation in which some input and state constraints are to be complied with predefined probability levels. The method considers a nonlinear relation between the uncertain input and the constrained variables. It also involves efficient algorithms so as to compute the probabilities and, simultaneously, the gradients through integration by collocation in finite elements. The formulation of single or joint probability limits incorporates the issue of feasibility and the contemplation of trade-off between robustness and profitability regarding the objective function values. The approach is relevant to all cases when uncertainty can be described by any kind of joint correlated multivariate distribution function. Thus, chance constrained programming is a promising technique in solving optimization problems under uncertainty in system engineering. The potential and the efficiency of the presented systematic methodology, which assumes a strict monotonic relationship between the uncertain input and the uncertain constrained output, are illustrated with application to a reactive batch distillation processes under uncertainty.     

采用结构进化策略的Lagrange乘子法优化换热网络

针对罚函数法处理有约束问题时存在的不足,采用Lagrange乘子法优化换热网络.为求解Lagrange函数方程组,根据确定性方法,提出最速下降法求解策略以及Powell法求解策略.通过极小值判断机制,保证Lagrange函数方程组的解是原换热网络目标函数值的极小值.根据实际工况,提出结构进化策略,与Lagrange乘子法相结合,实现了换热网络全局最优化.通过经典算例验证了两种求解策略的有效性、准确性以及结构进化策略的通用性.与文献结果进行对比,结果表明本算法具有较强的局部搜索能力以及全局搜索能力,能够找到更优的换热网络结构,有利于在工业生产中节约成本.     

An efficient constraint handling method with integrated differential evolution for numerical and engineering optimization

Constrained optimization problems are very important as they are encountered in many engineering applications. Equality constraints in them are challenging to handle due to tiny feasible region. Additionally, global optimization is required for finding global optimum when the objective function and constraints are nonlinear. Stochastic global optimization methods can handle non-differentiable and multi-modal objective functions. In this paper, a new constraint handling method for use with such methods is proposed for solving equality and/or inequality constrained problems. It incorporates adaptive relaxation of constraints and the feasibility approach for selection. The recent integrated differential evolution (IDE) with the proposed constraint handling technique is tested for solving benchmark problems with constraints, and then applied to many chemical engineering application problems with equality and inequality constraints. The results show that the proposed constraint handling method with IDE (C-IDE) is reliable and efficient for solving constrained optimization problems, even with equality constraints.     

Electrochemical modelling using electroneutrality equation as a constraint

This paper deals with ionic species migration based on classical mass balance equations in conjunction with a charge balance equation and the electroneutrality condition. Two new methods are proposed to apply the electroneutrality condition, avoiding the elimination of one of the conservation equations of chemical species and permitting to explicitly apply boundary conditions on all species. The first method is based on variational principles and the second on a Lagrange multiplier. It is shown that the new methods are analytically equivalent to the more standard method in which one conservation equation is eliminated without any other consideration. The variational method is more complex to implement than the Lagrange multiplier method. The new methods were applied to a multi-ionic problem together with the charge conservation equation without capacitive effects. Different kinds of boundary conditions were applied: Neumann, Dirichlet and a nonlinear case based on Butler–Volmer kinetics. All methods gave the same results for non-complex problems. In the case of complex problems including chemical equilibrium between each ionic species, it was found that more investigations are necessary even for more conventional methods.     

应用遗传算法求解多相化学反应体系的相稳定性及平衡组成

基于吉布斯自由能最小法原理,研究用遗传算法求解复杂化学体系的平衡问题。将相数和相态未知体系的平衡计算问题分解为两步最优化问题:假定相态数目下的吉布斯自由能最小化问题和相稳定性判据问题。通过序贯加入新相和用遗传算法交替求解两个最优化子问题,可得到最终体系的相数、相态和平衡组成。对合成甲醇和酯化反应两个多相复杂体系进行了的计算,结果验证了本研究方法的有效性。     

Improving scenario decomposition algorithms for robust nonlinear model predictive control

This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.     

Optimization-based design of reactive distillation columns using a memetic algorithm

The design optimization of reactive distillation columns (RDC) is characterized by complex nonlinear constraints, nonlinear cost functions, and the presence of many local optima. The standard approach is to use MINLP solvers that work on a superstructure formulation where structural decisions are represented by discrete variables and lead to an exponential increase in the computational effort. The mathematical programming (MP) methods which solve the continuous sub-problems provide only one local optimum which depends strongly on the initialization. In this contribution a memetic algorithm (MA) is introduced and applied to the global optimization of four different formulations of a computational demanding real-world design problem. An evolution strategy addresses the global optimization of the design decisions, while continuous sub-problems are efficiently solved by a robust MP solver. The MA is compared to MINLP techniques. It is the only algorithm that finds the global solution in reasonable times for all model formulations.     

Interior point SQP strategies for large-scale,structured process optimization problems

Successive quadratic programming (SQP) has been the method of choice for the solution of many nonlinear programming problems in process engineering. However, for the solution of large problems with SQP based codes, the combinatorial complexity associated with active set quadratic programming (QP) methods can be a bottleneck in exploiting the problem structure. In this paper, we examine the merits of incorporating an interior point QP method within an SQP framework. This provides a novel interpretation of popularly used predictor-corrector interior point (IP) methods. The resulting large-scale SQP algorithm, with an interior point QP, also allows us to demonstrate significant computational savings on problems drawn from optimal control and nonlinear model predictive control.