Joint optimization of number of wells,well locations and controls using a gradient-based algorithm

This paper presents a detailed algorithm for solving the general well-placement optimization problem in which the number of wells, their locations and rates are simultaneously optimized with an efficient gradient-based algorithm. The proposed well-placement optimization algorithm begins by placing a large number of wells in the reservoir, where, the well rates are the optimization variables. During iterations of the algorithm, most of the wells are eliminated by setting their rates to zero. The remaining wells and their controls determine the optimal number of wells, their optimum locations and rates. The well-placement algorithm consists of two optimization stages. In the initialization stage, the appropriate total reservoir production rate (or the total injection rate) for the set of to-be-optimized producers (or injectors) is estimated by maximizing the net-present-value for the specified operational life of the reservoir. In the second stage, a modified net-present-value functional which also considers the drilling cost of the wells is maximized subject to the a total rate constraint determined in the initialization stage. Both stages of the algorithm use gradient projection to enforce the linear and bound constraints, where the required gradients are computed with the adjoint method. The bottomhole pressure constraints on the wells are enforced using a practical approach. The applicability and robustness of our well-placement algorithm is discussed through several example problems.     

A nested, simultaneous approach for dynamic optimization problems-II: the outer problem

In a previous paper (Tanartkit, P. and Biegler, L. T. (1996) A nested, simultaneous approach for dynamic optimization problems - I. Comput. Chem. Eng. 20(6/7), 735–741), we introduced and demonstrated a general framework for solving dynamic optimization using bilevel programming. This framework decouples the element placement from the optimal control procedure and leads to a more robust algorithm. The optimization problem is replaced by two connected but simpler formulations, the inner and outer problems. The inner problem is essentially a dynamic optimization with fixed time steps. On the other hand, the outer problem adjusts the time step given the gradient information from the inner counterpart. By coupling a well-implemented collocation solver with reduced Hessian successive quadratic programming (SQP), we are able to tackle the inner part of the system in an efficient and stable fashion for both initial value and boundary value problems. However, the overall success of the algorithm still depends on robustness and performance of the outer problem. In this article this is achieved by combining a bundle underestimator with SQP. Also included in this article are different options of obtaining subgradients for the outer problem via sensitivity analysis and finite difference schemes. Here a decomposition is presented by taking advantage of the inner problem structure to reduce computational expense of the sensitivity evaluation. We will also address the limitations and properties involved in both schemes. In the final segment of the paper the focus is shifted to the issue of finite element addition. By utilizing insight from optimal control theory, we develop a systematic procedure for element addition with a rigorous stopping criterion. Finally, examples are given to illustrate the effectiveness and potential of the algorithm.     

Numerical optimization of heat recovery steam cycles: Mathematical model, two-stage algorithm and applications

Most of the advanced integrated energy systems need a heat recovery steam cycle (HRSC), either fired or unfired, that recovers the waste heat from gas turbines and process units in order to generate electric power and supply mechanical power to compressors, heat to endothermic processes, and steam to external users. The key feature of such HRSCs is the integration between the heat recovery steam generator (HRSG) and the external heat exchangers. This paper presents a rigorous mathematical programming model, a linear approximation, and a two-stage algorithm for optimizing the design of integrated HRSGs and HRSCs, simultaneously considering the HRSG together with the heat recovery steam network and the intensive steam cycle variables. A detailed application of the methodology is described for an integrated gasification combined cycle plant with CO₂ capture and results for other interesting plants are reported. A significant efficiency gain is obtained with respect to usual practice designs.     

A multi-priority hierarchical optimization method for double-layer model predictive control

Considering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double-layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady-state optimization layer of double-layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady-state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady-state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.     

一种提高微生物发酵单位的新优化控制策略

为了提高微生物发酵单位,提出了支持向量机（SVM）与基于实数编码遗传算法（RGA）相互耦联的优化控制策略。为解决发酵参数之间的耦合问题,进一步引入了模式的概念。SVM建立微生物发酵过程的预估模型,RGA以此模型为适应度函数计算最优控制模式。此策略应用于青霉素发酵过程的优化控制,效价比没有实施优化控制策略的生产结果提高了22.88％。     

Integrated design and control under uncertainty: Embedded control optimization for plantwide processes

High performance processes should operate close to design boundaries and specification limits, while still guaranteeing robust performance without design constraint violations. Since design chemical process is operating close to tighter boundaries safely; much attention has been devoted to integrating design and control, in which the design decisions, dynamics, and control performance are considered simultaneously in some optimal fashion. However, rigorous methods for solving design and control simultaneously lead to challenging mathematical formulations which easily become computationally intractable. In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced (Malcolm et al., 2007). We showed that substantial problem size reduction can be achieved by embedding control for specific process designs. In this paper, we extend the embedded control methodologies to plantwide flowsheet. The case study for the reactor-column flowsheet will demonstrate the current capabilities of the methodology for integrating design and control under uncertainty.     

Operations optimization and control design for a petroleum distillation process

Process automation involves both steady-state optimization and feedback control. In this paper, the economic optimum steady-state operating conditions for a petroleum crude distillation process are determined using a tray-to-tray model. This model is used to evaluate the steady-state operation achieved with several multivariable process control designs. Only one controller design was found to result in essentially optimum steady-state operation; others deviated significantly. The method applied in this paper relies on a knowledge of the key disturbances occurring in the process; good economic performance may not be obtained should other disturbance types occur. The results demonstrate the importance of implementing the operations optimization through a control strategy which will respond properly to disturbances.     

分批补料反应过程的非固定终端经济优化控制

针对批次生产周期不确定问题,提出一种非固定终端的经济优化控制方法。首先采用经济模型预测控制方法,用收益最大化的经济型目标函数代替终端约束,并将批次生产周期纳入被优化变量,建立动态经济优化问题,并通过对每个控制变量进行有差异的参数化,将动态优化问题转化为非线性规划（NLP）问题;然后使用内点罚函数法求解含非线性约束的优化问题,得到的最优控制序列和最佳批次生产周期,可将不确定扰动带来的损失降低到最小。其次采用非固定预测时域的滚动时域控制方法,不仅提高多变量系统的协同控制能力,而且根据实时预测终端产品产量不断优化更新关键操纵变量的控制分段函数的分割数及控制序列,从而可灵活优化操纵变量和操作时间的轨迹。最后在苯胺加氢过程上进行了批次优化控制性能测试,测试结果表明,非固定终端的经济优化控制从批次的总生产效益角度来优化每个批次生产的操作条件,实现批次反应过程生产时间与经济效益的最优化管理。     

Distributed control and optimization of process system networks:A review and perspective

Large-scale and complex process systems are essentially interconnected networks. The automated operation of such process networks requires the solution of control and optimization problems in a distributed manner. In this approach, the network is decomposed into several subsystems, each of which is under the supervision of a corresponding computing agent(controller, optimizer). The agents coordinate their control and optimization decisions based on information communication among them. In recent years, algorithms and methods for distributed control and optimization are undergoing rapid development. In this paper, we provide a comprehensive, up-to-date review with perspectives and discussions on possible future directions.     

PGS-COM: A hybrid method for constrained non-smooth black-box optimization problems: Brief review,novel algorithm and comparative evaluation

In the areas of chemical processes and energy systems, the relevance of black-box optimization problems is growing because they arise not only in the optimization of processes with modular/sequential simulation codes but also when decomposing complex optimization problems into bilevel programs. The objective function is typically discontinuous, non-differentiable, not defined in some points, noisy, and subject to linear and nonlinear relaxable and unrelaxable constraints. In this work, after briefly reviewing the main available direct-search methods applicable to this class of problems, we propose a new hybrid algorithm, referred to as PGS-COM, which combines the positive features of Constrained Particle Swarm, Generating Set Search, and Complex. The remarkable performance and reliability of PGS-COM are assessed and compared with those of eleven main alternative methods on twenty five test problems as well as two challenging process engineering applications related to the optimization of a heat recovery steam cycle and a styrene production process.     

精细化工发展的关键技术--模型化、控制与优化研究

过程模型化、控制和优化技术已成为制约我国精细化工发展的瓶颈之一.分析了我国精细化工的现状、影响未来发展的主要因素、内在特性,及其对过程模型化、控制与优化研究的影响与需求,综述了精细化工过程模型化、控制和优化的国内外研究情况.指出了我国精细化工过程模型化、控制和优化研究的研究原则,并提出了相应的研究方案.     

水泥工厂智能优化控制系统的研究与应用

智能优化控制系统是一种智能计算机软件系统,运用人类专家的经验和知识,将先进的控制理论与人工智能技术深度融合,实现水泥生产核心工艺环节的自动控制和操作,提高水泥生产自动化水平,降低操作人员劳动强度,智能优化控制系统是实现智能水泥工厂“自控制,自执行”的重要产品之一。本文主要内容包括系统简介、系统架构和系统功能,同时结合具体的应用案例重点介绍主要控制模块以及使用效果。     

乙二醇脱水和精制系统先进控制与优化

在对过程机理深入分析的基础上,通过推断控制、解耦控制、专家控制等先进控制策略的开发和实施,解决了乙二醇脱水和精制系统存在的具体问题,并根据所建立的准确工业过程模型进行了操作条件优化。表明了先进控制和优化技术的实施显著提高了乙二醇脱水和精制系统操作的稳定性,有效降低系统能耗并延长了运转周期。     

高纯度精馏过程的集成控制与在线优化策略

For high-purity distillation processes, it is difficult to achieve a good direct product quality control using traditional pro-portional-integral-differential (PID) control or multivariable predictive control technique due to some difficulties, such as long re-sponse time, many un-measurable disturbances, and the reliability and precision issues of product quality soft-sensors. In this paper, based on the first principle analysis and dynamic simulation of a distillation process, a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable. Correspondingly, a new strategy with integrated control and on-line optimization is developed, which consists of model predictive control of the split ratio, surrogate model based on radial basis function neural network for optimization, and modified differential evolution optimization algorithm. With the strategy, the process achieves its steady state quickly, so more profit can be obtained. The proposed strategy has been successfully applied to a gas separation plant for more than three years, which shows that the strategy is feasible and effective.     

有旁路换热网络全周期节能的动态优化控制实现方法

为了实现换热网络的全周期持续节能,在网络上设置旁路从而增加其控制自由度,同时设计一定的裕量来提供优化控制的操作空间。为了较好地利用旁路调节和裕量空间,提出一种基于换热网络动态模型的在线优化控制方法,巧妙地结合原有常规控制回路,不但扩大了优化控制的可行域,并且满足原常规控制回路的精度要求。该方法以换热网络一定周期内的累积费用最小为目标函数,同时考虑扰动对换热网络的影响,在满足工艺条件的基础上,求解最佳旁路开度,以实现换热网络的持续节能。由于采用闭环校正、迭代计算和滚动优化的实施方法,始终把优化建立在实际的基础上,尽管它每次不一定能得到全局最优解,然而使得实际控制结果达到最优。最后,以某炼油厂的常减压脱盐前换热网络为具体的研究对象,说明所提方法的有效性和使用前景。     

A decomposition algorithm for simultaneous scheduling and control of CSP systems

We present a decomposition algorithm to perform simultaneous scheduling and control decisions in concentrated solar power (CSP) systems. Our algorithm is motivated by the need to determine optimal market participation strategies at multiple timescales. The decomposition scheme uses physical insights to create surrogate linear models that are embedded within a mixed‐integer linear scheduling layer to perform discrete (operational mode) decisions. The schedules are then validated for physical feasibility in a dynamic optimization layer that uses a continuous full‐resolution CSP model. The dynamic optimization layer updates the physical variables of the surrogate models to refine schedules. We demonstrate that performing this procedure recursively provides high‐quality solutions of the simultaneous scheduling and control problem. We exploit these capabilities to analyze different market participation strategies and to explore the influence of key design variables on revenue. Our results also indicate that using scheduling algorithms that neglect detailed dynamics significantly decreases market revenues. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2408–2417, 2018     

A global optimization algorithm for nonconvex generalized disjunctive programming and applications to process systems

A global optimization algorithm for nonconvex Generalized Disjunctive Programming (GDP) problems is proposed in this paper. By making use of convex underestimating functions for bilinear, linear fractional and concave separable functions in the continuous variables, the convex hull of each nonlinear disjunction is constructed. The relaxed convex GDP problem is then solved in the first level of a two-level branch and bound algorithm, in which a discrete branch and bound search is performed on the disjunctions to predict lower bounds. In the second level, a spatial branch and bound method is used to solve nonconvex NLP problems for updating the upper bound. The proposed algorithm exploits the convex hull relaxation for the discrete search, and the fact that the spatial branch and bound is restricted to fixed discrete variables in order to predict tight lower bounds. Application of the proposed algorithm to several example problems is shown, as well as a comparison with other algorithms.     

Supersaturation tracking for the development,optimization and control of crystallization processes

Supersaturation is the key driving force in crystallization operations, determining nucleation and growth kinetics, and heavily influencing physical mechanisms such as agglomeration. Therefore, knowledge of the bulk supersaturation during crystallization can greatly enhance process understanding and optimization.