汽油调合调度优化

采用连续时间建模方法,建立了一种新的汽油非线性调合和调度集成优化的混合整数非线性规划（MINLP）模型,克服了当前在油品调合调度中采用线性调合模型或者将非线性调合过程和调度分开优化的缺陷。针对建立MINLP模型的特点,将原MINLP问题转化为求解一系列的混合整数线性规划（MILP）模型,避免了直接求解MINLP模型的复杂性。最后以某大型炼油企业为例,验证了模型和算法的实用性。     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

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     

Scheduling of cracking production process with feedstocks and energy constraints

This paper addresses the short-term scheduling problem for the ethylene cracking process with feedstocks and energy constraints. The cracking production of ethylene is a process with units that have decaying performance, requiring periodic cleanup to restore their performance. Under the condition of limited feedstocks, the production operating mode of the cracking furnaces is to keep yields constant by continuously increasing the coil temperature. We present a hybrid MINLP/GDP formulation based on continuous-time representation for the scheduling problem over a finite time horizon. In order to solve the proposed model, which is reformulated as an MINLP model, an improved outer approximation algorithm with multi-generation cuts and problem-dependent integer cuts are developed to solve real large-scale problems. Numerical examples are presented to illustrate the application of the model. Based on analyzing the optimal solution and sensitivity of the model, some conclusions are obtained to provide useful suggestions for real cracking process production.     

多产品间歇过程的分层次在线调度--一种数学规划与遗传算法的混合算法

In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.     

A projection‐based method for production planning of multiproduct facilities

An algorithm is presented for identifying the projection of a scheduling model's feasible region onto the space of production targets. The projected feasible region is expressed using one of two mixed‐integer programming formulations, which can be readily used to address integrated production planning and scheduling problems that were previously intractable. Production planning is solved in combination with a surrogate model representing the region of feasible production amounts to provide optimum production targets, while a detailed scheduling is solved in a rolling‐horizon manner to define feasible schedules for meeting these targets. The proposed framework provides solutions of higher quality and yields tighter bounds than previously proposed approaches. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Global optimization for scheduling refinery crude oil operations

In this work we present an outer-approximation algorithm to obtain the global optimum of a nonconvex mixed-integer nonlinear programming (MINLP) model that is used to represent the scheduling of crude oil movement at the front-end of a petroleum refinery. The model relies on a continuous time representation making use of transfer events. The proposed algorithm focuses on effectively solving a mixed-integer linear programming (MILP) relaxation of the nonconvex MINLP to obtain a rigorous lower bound (LB) on the global optimum. Cutting planes derived by spatially decomposing the network are added to the MILP relaxation of the original nonconvex MINLP in order to reduce the solution time for the MILP relaxation. The solution of this relaxation is used as a heuristic to obtain a feasible solution to the MINLP which serves as an upper bound (UB). The lower and upper bounds are made to converge to within a specified tolerance in the proposed outer-approximation algorithm. On applying the proposed technique to test examples, significant savings are realized in the computational effort required to obtain provably global optimal solutions.     

Short‐term scheduling of a large‐scale oil‐refinery operations: Incorporating logistics details

Refineries are increasingly concerned with improving the scheduling of their operations to achieve better economic performances by minimizing quality, quantity, and logistics give away. In this article, we present a comprehensive integrated optimization model based on continuous‐time formulation for the scheduling problem of production units and end‐product blending problem. The model incorporates quantity, quality, and logistics decisions related to real‐life refinery operations. These involve minimum run‐length requirements, fill‐draw‐delay, one‐flow out of blender, sequence‐dependent switchovers, maximum heel quantity, and downgrading of better quality product to lower quality. The logistics giveaways in our work are associated with obtaining a feasible solution while minimizing violations of sequence‐dependent switchovers and maximum heel quantity restrictions. A set of valid inequalities are proposed that improves the computational performance of the model significantly. The formulation is used to address realistic case studies where feasible solutions are obtained in reasonable computational time. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed‐integer dynamic optimization problem or a large‐scale mixed‐integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed‐integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2379–2406, 2013     

Continuous‐time modeling and global optimization approach for scheduling of crude oil operations

Scheduling of crude oil operations is a critical and complicated component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. Moreover, blending with less expensive crudes can significantly increase profit margins. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transforms the problem into a challenging, nonconvex, and mixed‐integer nonlinear programming (MINLP) optimization model. Two primary contributions have been made. First, the authors developed a novel unit‐specific event‐based continuous‐time MINLP formulation for this problem. Then they incorporated realistic operational features such as single buoy mooring (SBM), multiple jetties, multiparcel vessels, single‐parcel vessels, crude blending, brine settling, crude segregation, and multiple tanks feeding one crude distillation unit at one time and vice versa. In addition, 15 important volume‐based or weight‐based crude property indices are also considered. Second, they exploited recent advances in piecewise‐linear underestimation of bilinear terms within a branch‐and‐bound algorithm to globally optimize the MINLP problem. It is shown that the continuous‐time model results in substantially fewer bilinear terms. Several examples taken from the work of Li et al. are used to illustrate that (1) better solutions are obtained and (2) ε‐global optimality can be attained using the proposed branch‐and‐bound global optimization algorithm with piecewise‐linear underestimations of the bilinear terms. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Sustainable design and synthesis of algae‐based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration

The superstructure optimization of algae‐based hydrocarbon biorefinery with sequestration of CO₂ from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed‐integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two‐stage heuristic solution algorithm to solve the nonconvex MINLP model. Finally, the bicriteria optimization problem is solved with ε‐constraint method, and the resulting Pareto‐optimal curve reveals the trade‐off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO₂‐eq per gallon. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1599–1621, 2013     

A Data‐Driven Gaussian Process Regression Model for Two‐Chamber Microbial Fuel Cells

Rapidly and accurately modeling of microbial fuel cells (MFCs) plays an important role not only in thorough understanding of the effects of operating conditions on system performance, but also in the successful implementation of real‐time maximization of power output. Although the first principle electrochemical model has better generalization performance, it is often time‐consuming for model construction and is hard to real‐time application. In this study, a nonparametric Gaussian process regression (GPR) model is used to capture the nonlinear relationship between operating conditions and output voltage in the MFCs. A simple online learning strategy is proposed to recursively update the hyper‐parameters of the GPR model. The applicability and effectiveness of the proposed method is validated by both the simulation and experimental datasets from the acetate and the glucose and glutamic acid two‐chamber MFCs. The results illustrate that the online GPR model provides a promising method for capturing the complex nonlinearity phenomenon in MFCs, which can be greatly helpful for further real‐time optimization of MFCs.     

不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

Prediction of the Profit Function for Industrial 2‐Keto‐L‐Gulonic Acid Cultivation

The profit function is the generic criterion to describe the cost effect of a batch process. To focus on the prediction of the profit function for 2‐keto‐L‐gulonic acid (2‐KGA) cultivation, which is potentially applicable for process monitoring and optimal scheduling, rolling learning‐prediction (RLP) based on a support vector machine (SVM) is applied. The RLP implies that the SVM training database is rolling updated as the batch of current interest proceeds, and the SVM learning is then repeated for the prediction. The database is further updated after termination of a batch. The updating procedures are investigated in detail. Pseudo‐online prediction is carried out using the data from industrial‐scale 2‐KGA cultivation under actual and hypothetical inoculation sequences. The results indicate that the average relative prediction error is less than 5 % in the later phase of fermentation in all inoculation sequences.     

炼油厂氢气系统优化调度及其应用

建模求解,并通过实例证明了模型的有效性和可行性,为实际的炼油厂氢气系统优化调度起到了指导作用。     

Centralized–decentralized optimization for refinery scheduling

This paper presents a novel decomposition strategy for solving large scale refinery scheduling problems. Instead of formulating one huge and unsolvable MILP or MINLP for centralized problem, we propose a general decomposition scheme that generates smaller sub-systems that can be solved to global optimality. The original problem is decomposed at intermediate storage tanks such that inlet and outlet streams of the tank belong to the different sub-systems. Following the decomposition, each decentralized problem is solved to optimality and the solution to the original problem is obtained by integrating the optimal schedule of each sub-systems. Different case studies of refinery scheduling are presented to illustrate the applicability and effectiveness of the proposed decentralized strategy. The conditions under which these two types of optimization strategies (centralized and decentralized) give the same optimal result are discussed.     

Optimal synthesis of p‐xylene separation processes based on crystallization technology

This article addresses the synthesis and optimization of crystallization processes for p‐xylene recovery for systems with feed streams of high concentration, a case that arises in hybrid designs where the first step is commonly performed by adsorption. A novel superstructure and its corresponding mixed‐integer nonlinear programming (MINLP) model are proposed. The distinct feature of this superstructure is the capability to generate optimum or near optimum flow sheets for a wide range of specifications of p‐xylene compositions in the feed stream of the process. To cope with the complexity of the MINLP model, a two‐level decomposition approach, consisting of the solution of an aggregated model and a detailed model, is proposed. The results obtained show good performance of the decomposition strategy, and the optimal flow sheets and p‐xylene recoveries are in agreement with the results reported in patents. © 2008 American Institute of Chemical Engineers AIChE J, 2009     

Real‐time dynamic hoist scheduling for multistage material handling process under uncertainties

Multistage material handling processes are broadly used for manufacturing various products/jobs, where hoists are commonly used to transport inline products according to their processing recipes. When multiple types of jobs with different recipes are simultaneously and continuously handled in a production line, the hoist movement scheduling should be thoroughly investigated to ensure the operational feasibility of every job inline and in the meantime to maximize the productivity if possible. The hoist scheduling will be more complicated, if uncertainties of new coming jobs are considered, that is, the arrival time, type, recipe, and number of new jobs are totally unknown and unpredictable before they join the production line. To process the multiple jobs already inline and the newly added jobs, the hoist movements must be swiftly rescheduled and precisely implemented whenever new job(s) come. Because a reschedule has to be obtained online without violating processing time constraints for each job, the solution identification time for rescheduling must be taken into account by the new schedule itself. All these stringent requisites motivate the development of real‐time dynamic hoist scheduling (RDHS) targeting online generation of reschedules for productivity maximization under uncertainties. Hitherto, no systematic and rigorous methodologies have been reported for this study. In this article, a novel RDHS methodology has been developed, which takes into account uncertainties of new coming jobs and targets real‐time scheduling optimality and applicability. It generally includes a reinitialization algorithm to accomplish the seamless connection between the previous scheduling and rescheduling operations, and a mixed‐integer linear programming model to obtain the optimal hoist reschedule. The RDHS methodology addresses all the major scheduling issues of multistage material handling processes, such as multiple recipes, multiple jobs, multicapacity processing units, diverse processing time requirements, and even optimal processing queue for new coming jobs. The efficacy of the developed methodology is demonstrated through various case studies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 465–482, 2013     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015     

Inventory pinch based,multiscale models for integrated planning and scheduling‐part II: Gasoline blend scheduling

Integration of planning and scheduling optimizes simultaneous decisions at both levels, thereby leading to more efficient operation. A three‐level discrete‐time algorithm which uses nonlinear models and integrates planning and detailed scheduling is introduced: first level optimizes nonlinear blend models via multiperiod nonlinear programming (NLP), where period boundaries are initially determined by the inventory pinch points; second level uses fixed recipes (from the first level) in a multiperiod mixed‐integer linear program to determine first an optimal production plan and then to optimize an approximate schedule which minimizes the total number of switches in blenders and swing tanks; third level computes detailed schedules that adhere to inventory constraints computed in the approximate schedule. If inventory infeasibilities appear at the second or the third level, the first‐level periods are subdivided and blend recipes are reoptimized. Algorithm finds the same or better solutions and is substantially faster than previously published full‐space continuous‐time model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2475–2497, 2014