A novel two-stage Lagrangian decomposition approach for refinery production scheduling with operational transitions in mode switching☆

To address large scale industrial processes, a novel Lagrangian scheme is proposed to decompose a refinery scheduling problem with operational transitions in mode switching into a production subproblem and a blending and delivery subproblem. To accelerate the convergence of Lagrange multipliers, some auxiliary constraints are added in the blending and delivery subproblem. A speed-up scheme is presented to increase the efficiency for solving the production subproblem. An initialization scheme of Lagrange multipliers and a heuristic algorithm to find feasible solutions are designed. Computational results on three cases with different lengths of time hori-zons and different numbers of orders show that the proposed Lagrangian scheme is effective and efficient.     

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     

Improved continuous-time model for gasoline blend scheduling

This work introduces a reduced-size continuous-time model for scheduling of gasoline blends. Previously published model has been modified by (i) introducing new model features (penalty for deliveries in order to reduce sending material from different product tanks to the same order, product and blender-dependent minimum setup times, maximum delivery rate from component tanks, threshold volume for each blend), (ii) by reducing the number of integer variables, and (iii) by adding lower bounds on the blend and switching costs, which significantly improve convergence. Nonlinearities are introduced by ethyl RT-70 equations for octane blending. Medium-size linear problems (two blenders, more than 20 orders, 5 products) are solved to optimality within one or two minutes. Previously unsolved large scale blending problems (more than 35 orders, 5 product, 2 or 3 blenders) have also been solved to less than 0.5% optimality gap.     

A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

ABSTRACT

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

Sequential methodology for integrated optimization of energy and water use during batch process scheduling

Though commonly encountered in practice, energy and water minimization simultaneously during batch process scheduling has been largely neglected in literature. In this paper, we present a novel framework for incorporating simultaneous energy and water minimization in batch process scheduling. The overall problem is decomposed into three parts - scheduling, heat integration, and water reuse optimization - and solved sequentially. Our approach is based on the precept that in any production plant, utilities (energy and water) consumption is subordinate to the production target. Hence, batch scheduling is solved first to meet an economic objective function. Next, alternate schedules are generated through a stochastic search-based integer cut procedure. For each resulting schedule, minimum energy and water reuse targets are established and networks identified. As illustrated using two well-known case studies, a key feature of this approach is its ability to handle problems that are too complex to be solved using simultaneous methods.     

A pattern matching method for large‐scale multipurpose process scheduling

This article proposes a novel pattern matching method for the large‐scale multipurpose process scheduling with variable or constant processing times. For the commonly used mathematical programming models, large‐scale scheduling with long‐time horizons implies a large number of binary variables and time sequence constraints, which makes the models intractable. Hence, decomposition and cyclic scheduling are often applied to such scheduling. In this work, a long‐time horizon of scheduling is divided into two phases. Phase one is duplicated from a pattern schedule constructed according to the principle that crucial units work continuously, in parallel and/or with full load as possible, exclusive of time‐consuming optimization. Phase two involves a small‐size subproblem that can be optimized easily by a heuristic method. The computational effort of the proposed method does not increase with the problem size. The pattern schedule can be not only used for production/profit maximization but also for makespan estimation and minimization. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Multi-product batch scheduling with intermediate due dates using priced timed automata models

In this contribution, we discuss an extension of the earlier work on scheduling using reachability analysis of timed automata (TA) models, specifically addressing the problem of tardiness minimization. In the TA-based approach the resources, recipes and additional timing constraints are modeled independently as sets of priced timed automata. The sets of individual automata are synchronized by means of synchronization labels and are composed by parallel composition to form a global automaton. The global automaton has an initial location where no operations have been started and at least one target location where all operations that are required to produce the demanded quantities of end-products within the specified due dates have been finished. A cost-optimal symbolic reachability analysis is performed on the composed automaton to derive schedules with the objective of minimizing tardiness. The model formulation is extended to include release dates of the raw materials and due dates of the production orders. The meeting of due dates is modeled by causing additional costs (e.g. penalties for late delivery and storage costs for early production). The modeling approach and the performance of the approach are tested for two different case studies and the results are compared with that of a MILP formulation solved using the standard solver CPLEX. The numerical experiments demonstrate, that the TA-based approach is competitive compared to standard commercial solvers and good feasible solutions are obtained with considerably reduced computational effort.     

A mixed integer programming approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimisation structure to aid the operational decision-making of scheduling activities in a real-world pipeline scenario. The pipeline connects an inland refinery to a harbour, conveying different types of oil derivatives. The optimisation structure is developed based on mixed integer linear programming (MILP) with uniform time discretisation, but the MILP well-known computational burden is avoided by the proposed decomposition strategy, which relies on an auxiliary routine to determine temporal constraints, two MILP models, and a database. The scheduling of operational activities takes into account product availability, tankage constraints, pumping sequencing, flow rate determination, and a variety of operational requirements. The optimisation structure main task is to predict the pipeline operation during a limited scheduling horizon, providing low cost operational procedures. Illustrative instances demonstrate that the optimisation structure is able to define new operational points to the pipeline system, providing significant cost saving.     

Wastewater minimization in multipurpose batch plants with a regeneration unit: Multiple contaminants

Wastewater minimization can be achieved by employing water reuse opportunities. This paper presents a methodology to address the problem of wastewater minimization by extending the concept of water reuse to include a wastewater regenerator. The regenerator purifies wastewater to such a quality that it can be reused in other operations. This further increases water reuse opportunities in the plant, thereby significantly reducing freshwater demand and effluent generation. The mathematical model determines the optimum batch production schedule that achieves the minimum wastewater generation within the same framework. The model was applied to two case studies involving multiple contaminants and wastewater reductions of 19.2% and 26% were achieved.     

化学工业中反应器以及容器清洗的最佳用水方案

概述了清洗生产纺织行业辅助产品的反应器和容器中用水的最佳方案。先用空气(约2．2t／a)作第一次清洗,既回收了产品又减少了废水污染负荷;再利用水处理工厂可处理清洗水并重新回用。在清洗反应器时减少了60％的用水量,清洗容器时减少了90％,这些都归因于：1)采用新的生产方案,减少工艺中的清洗环节;2)引入下一步生产中反应器所需的清洁度的概念;3)使用新的容器清洗程序。由于被处理的水量减少,污染负荷降低,意味着只需一个更小的废水处理工厂同时消耗更少的化学产品、减少了以后要处理的污泥量,最终达到合理用水的目的。     

Optimal planning and scheduling of batch plants with operational uncertainties: An industrial application to Baker's yeast production

In this work, the mixed integer linear programming (MILP) model developed in Orçun et. al 1996 for optimal planning and scheduling of batch process plants under uncertain operating conditions is further improved to deal also with discrete probability functions. Furthermore, the logic behind integrating the processing uncertainties within the MILP model is implemented on the variations in the production volumes that can be faced in some batch processes such as Baker's yeast production. The modified model is tested on Baker's yeast production plant data to illustrate the effect of uncertainties on the production planning and scheduling. The results show that the plant production will be improved by 20% when the optimal production planning and scheduling is utilized by fine tuning the degree of risk the management can resist. An example on how a process design engineer may utilize such an MILP model for optimal planning and scheduling of batch process plant and identify plant problems, such as the bottleneck operations, is also included. A simulation type analysis on how to improve the processing site, i.e. the effect of introducing an extra operator to the bottleneck operation, is also demonstrated in this work using the available plant data.     

Refinery continuous-time crude scheduling with consideration of long-distance pipeline transportation

In reality, crudes from unloading storage tanks at a docking berth may experience long-distance pipeline transportation to refinery charging tanks before their processing by crude distillation units. Such pipeline transportations cause significant transport delay and crude holdup that will substantially affect plant production performance. Unfortunately, current short-term crude scheduling studies have never systematically considered these issues. In this work, a new continuous-time crude scheduling model has been developed, which addresses the long-distance pipeline transportation and other realistic considerations such as brine settling and multiple jetties for crude unloading. The feeding of crudes into pipeline, and crude movements inside pipeline, as well as crude discharging to the receiving charging tanks are combined with the continuous time formulation of crude scheduling. The efficacy of the developed scheduling model has been demonstrated by three case studies including one industrial size example. An outer-approximation (OA) based iterative algorithm (Karuppiah et al., 2008) is implemented to successfully solve the case studies.     

Scheduling a multi-product pipeline network

Daily some millions barrels of oil are moved around the world in imports and exports and domestically within countries. While ships are the main mode for intercontinental transport, pipelines are the chief form of transcontinental transport, while regional and local transports is performed by trains and trucks. Despite high installation costs, pipelines are considered highly efficient as a mode for transporting large amounts of oil and oil products over long distances, because they offer lower operation costs, higher reliability rates, lower product loss rates, less environmental impact, and less susceptibility to adverse weather conditions than other modes. This study deals with a multi-product pipeline system that transports a set of oil products (diesel, gasoline and kerosene, for example), which have to be moved from points (operating areas) where they are produced or stored (refineries, terminals) to points where they are needed (other refineries, distribution centers, terminals, ports, customers) through a pipeline or set of pipelines.The present study contributes primarily by offering an efficient tool for the problem of scheduling multi-product pipeline networks. The methodology proposed takes the approach of discretizing both pipelines and planning horizon and combines an efficient MILP model with a post-processing heuristic. When compared with previous models, we propose a more efficient one in which the set of volumetric constraints is modeled in the form of knapsack cascading constraints and constraints on products in pipeline sections, which made for significantly improved performance in the experiments that were conducted. The proposed methodology thus constitutes an advance in terms of modeling the problem, making it feasible to solve problems increasingly close to the realities confronting oil industry operators.     

Recipe determination and scheduling of gasoline blending operations

Gasoline is a major contributor to the profit of a refinery. Scheduling gasoline‐blending operations is a critical and complex routine task involving tank allocation, component mixing, blending, product storage, and order delivery. Optimized schedules can maximize profit by avoiding ship demurrage, improving order delivery, minimizing quality give‐aways, avoiding costly transitions and slop generation, and reducing inventory costs. However, the blending recipe and scheduling decisions make this problem a nonconvex mixed‐integer nonlinear program (MINLP). In this article, we develop a slot‐based MILP formulation for an integrated treatment of recipe, specifications, blending, and storage and incorporate many real‐life features such as multipurpose product tanks, parallel nonidentical blenders, minimum run lengths, changeovers, piecewise constant profiles for blend component qualities and feed rates, etc. To ensure constant blending rates during a run, we develop a novel and efficient procedure that solves successive MILPs instead of a nonconvex MINLP. We use 14 examples with varying sizes and features to illustrate the superiority and effectiveness of our formulation and solution approach. The results show that our solution approach is superior to commercial solvers (BARON and DICOPT). © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Formulation of products from soybean oil

High quality shortenings and margarines may be produced using soybean oil as the only fat source or using soybean oil as the primary fat source with the addition of a small amount of hydrogenated cottonseed or palm oil to provide crystal stability. These shortenings and margarines are manufactured by direct hydrogenation or by blending hydrogenated and/or unhydrogenated base stocks. The properties of soybean oil preclude the need for processes other than hydrogenation and blending to produce most margarine and shortening products. It is possible to design an integrated base stock program in which a limited number of base stocks may be used jointly in margarine and shortening formulations. This type of base stock program results in fewer hydrogenation department heels and simplifies scheduling of the hydrogenation department as well as scheduling of overall operations. Solid fat index (SFI) is the analysis used for final product consistency control. While base stocks are blended to meet a final SFI requirement, this analysis is too time-consuming to be used in hydrogenation control and individual hydrogenation batches are controlled using refractometer number and congeal points. Finished product characteristics are a result of decisions that must be made regarding characteristics such as plastic range and AOM stability, which are incompatible.     

Integrated gasoline blending and order delivery operations: Part I. short‐term scheduling and global optimization for single and multi‐period operations

Gasoline is one of the most valuable products in an oil refinery and can account for as much as 60–70% of total profit. Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by avoiding ship demurrage, improving customer satisfaction, minimizing quality give‐aways, reducing costly transitions and slop generation, exploiting low‐quality cuts, and reducing inventory costs. In this article, we first introduce a new unit‐specific event‐based continuous‐time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed‐integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed‐integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%‐global optimality with modest computational effort. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2043–2070, 2016     

A cost‐effective model for the gasoline blend optimization problem

Gasoline blending is a critical process with a significant impact on the total revenues of oil refineries. It consists of mixing several feedstocks coming from various upstream processes and small amounts of additives to make different blends with some specified quality properties. The major goal is to minimize operating costs by optimizing blend recipes, while meeting product demands on time and quality specifications. This work introduces a novel continuous‐time mixed‐integer linear programming (MILP) formulation based on floating time slots to simultaneously optimize blend recipes and the scheduling of blending and distribution operations. The model can handle non‐identical blenders, multipurpose product tanks, sequence‐dependent changeover costs, limited amounts of gasoline components, and multi‐period scenarios. Because it features an integrality gap close to zero, the proposed MILP approach is able to find optimal solutions at much lower computational cost than previous contributions when applied to large gasoline blend problems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3002–3019, 2016