Dynamic hybrid simulation of batch processes driven by a scheduling module

Simulation is now a CAPE tool widely used by practicing engineers for process design and control. In particular, it allows various offline analyses to improve system performance such as productivity, energy efficiency, waste reduction, etc. In this framework, we have developed the dynamic hybrid simulation environment PrODHyS whose particularity is to provide general and reusable object-oriented components dedicated to the modeling of devices and operations found in chemical processes. Unlike continuous processes, the dynamic simulation of batch processes requires the execution of control recipes to achieve a set of production orders. For these reasons, PrODHyS is coupled to a scheduling module (ProSched) based on a MILP mathematical model in order to initialize various operational parameters and to ensure a proper completion of the simulation. This paper focuses on the procedure used to generate the simulation model corresponding to the realization of a scenario described through a particular scheduling.     

Optimization of steel production scheduling with complex time-sensitive electricity cost

Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.     

Integrated production planning and scheduling using a decomposition framework

To ensure the consistency between planning and scheduling decisions, the integrated planning and scheduling problem should be addressed. Following the natural hierarchy of decision making, integrated planning and scheduling problem can be formulated as bilevel optimization problem with a single planning problem (upper level) and multiple scheduling subproblems (lower level). Equivalence between the proposed bilevel model and a single level formulation is proved considering the special structure of the problem. However, the resulting model is still computationally intractable because of the integrality restrictions and large size of the model. Thus a decomposition based solution algorithm is proposed in this paper. In the proposed method, the production feasibility requirement is modeled through penalty terms on the objective function of the scheduling subproblems, which is further proportional to the amount of unreachable production targets. To address the nonconvexity of the production cost function of the scheduling subproblems, a convex polyhedral underestimation of the production cost function is developed to improve the solution accuracy. The proposed decomposition framework is illustrated through examples which prove the effectiveness of the method.     

Optimization of the heat plant of district energy systems

This paper presents a scheme to achieve structural and operational optimization for the heat plant in a district energy system. A district energy system consists of energy suppliers and consumers, district heating pipelines and heat storage facilities in a region. Production and consumption of energy and transport of energy as well as storage of heat are taken into account in the model. The problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall cost of the district energy system. Evaluation of the energy production cost is based on the daily operation for every season at the plant located at Suseo in Seoul, Korea. From the results of numerical simulations we can see that the district energy system is well approximated by the proposed model, and that the energy efficiency is improved by the application of the optimal operation conditions provided by the proposed model.     

Modeling and optimization for oil well production scheduling☆

In this paper, an oil wel production scheduling problem for the light load oil wel during petroleum field exploi-tation was studied. The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wel s in a given oil reservoir, subject to a number of constraints such as minimum up/down time limits and well grouping. The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost. Due to the NP-hardness of the problem, an improved par-ticle swarm optimization (PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately. Computational experiments on randomly generated instances were carried out to eval-uate the performance of the model and the algorithm's effectiveness. Compared with the commercial solver CPLEX, the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.     

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

With diversified requirements and varying manufacturing environments, the optimal production planning for a steelmill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming (MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome theweakness in solving the MINLP problem directly. The first one is to transformthe original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the originalmodel using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.     

关于炼钢用耐火材料优化的几个问题

概述了现代炼钢生产发展与优化的主要特点。为适应这些发展特点的要求 ,提出了耐火材料向“更长寿、无污染、功能化”方向发展的必要性观点     

A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants

This paper addresses the solution of simultaneous scheduling and planning problems in a production–distribution network of continuous multiproduct plants that involves different temporal and spatial scales. Production planning results in medium and long-term decisions, whereas production scheduling determines the timing and sequence of operations in the short-term. The production–distribution network is made up of several production sites distributing to different markets. The planning and scheduling model has to include spatial scales that go from a single production unit within a site, to a geographically distributed network. We propose to use two decomposition methods to solve this type of problems. One method corresponds to the extension of the bi-level decomposition of Erdirik-Dogan and Grossmann (2008) to multi-site, multi-market networks. A second method is a novel hybrid decomposition method that combines bi-level and spatial Lagrangean decomposition methods. We present four case studies to study the performance of the full space planning and scheduling model, the bi-level decomposition, and the bi-level Lagrangean method in profit maximization problems. Numerical results indicate that in large-scale problems, decomposition methods outperform the full space solution and that as problem size increases the hybrid decomposition method becomes faster than the bi-level decomposition alone.     

A multi-level simulation approach for the crude oil loading/unloading scheduling problem

An integrated approach for refinery production scheduling and unit operation optimization problems is presented. Each problem is at a different decision making layer and has an independent objective function and model. The objective function at the operational level is an on-line maximization of the difference between the product revenue and the energy and environmental costs of the main refinery units. It is modeled as an NLP and is constrained by ranges on the unit's operating condition as well as product quality constraints. The production scheduling layer is modeled as an MILP with the objective of minimizing the logistical costs of unloading the crude oil over a day-to-week time horizon. The objective function is a linear sum of the unloading, sea waiting, inventory, and setup costs. The nonlinear simulation model for the process units is used to find optimized refining costs and revenue for a blend of two crudes. Multiple linear regression of the individual crude oil flow rates within the crude oil percentage range allowed by the facility is then used to derive linear refining cost and revenue functions. Along with logistics costs, the refining costs or revenue are considered in the MILP scheduling objective function. Results show that this integrated approach can lead to a decrease of production and logistics costs or increased profit, provide a more intelligent crude schedule, and identify production level scheduling decisions which have a tradeoff benefit with the operational mode of the refinery.     

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.     

Optimal synthesis of compression refrigeration system using a novel MINLP approach

The optimal design of a compression refrigeration system (CRS) with multiple temperature levels is very important to chemical process industries and also represents considerable challenges in process systems engineering. In this paper, a general methodology for the optimal synthesis of the CRS, which simultaneously integrates CRS and Heat Exchanger Networks (HEN) to minimize the total compressor shaft work consumption based on an MINLP model, has been proposed. The major contribution of this method is in addressing the optimal design of refrigeration cycle with variable refrigeration temperature levels. The method can be used to make major decisions in the CRS design, such as the number of levels, temperature levels, and heat transfer duties. The performance of the developed methodology has been illustrated with a case study of an ethylene CRS in an industrial ethylene plant, and the optimal solution has been examined by rigorous simulations in Aspen Plus to verify its feasibility and consistency.     

A novel precedence-based and heuristic approach for short-term scheduling of multipurpose batch plants

Many continuous-time formulations have been proposed during the last decades for short-term scheduling of multipurpose batch plants. Although these models establish advantages over discrete-time representations, they are still inefficient in solving moderate-size problems, such as maximization of profit in long horizon, and minimization of makespan. Unlike existing literature, this paper presents a new precedence-based mixed integer linear programming (MILP) formulation for short-term scheduling of multipurpose batch plants. In the new model, multipurpose batch plants are described with a modified state-task network (STN) approach, and binary variables express the assignments and sequences of batch processing and storing. To eliminate the drawback of precedence-based formulations which commonly include large numbers of batches, an iterative procedure is developed to determine the appropriate number of batch that leads to global optimal solution. Moreover, four heuristic rules are proposed to selectively prefix some binary variables to 0 or 1, thereby reducing the overall number of binary variables significantly. To evaluate model performance, our model and the best models reported in the literature (S&K model and I&F model) are utilized to solve several benchmark examples. The result comparison shows that our model is more effective to find better solution for complex problems when using heuristic rules. Note that our approach not only can handle unlimited intermediate storage efficiently as well as the I&F model, but also can solve scheduling problems in limited intermediate storage more quickly than the S&K model.     

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.     

A time scale-bridging approach for integrating production scheduling and process control

In this paper, we propose a novel framework for integrating scheduling and nonlinear control of continuous processes. We introduce the time scale-bridging model (SBM) as an explicit, low-order representation of the closed-loop input–output dynamics of the process. The SBM then represents the process dynamics in a scheduling framework geared towards calculating the optimal time-varying setpoint vector for the process control system. The proposed framework accounts for process dynamics at the scheduling stage, while maintaining closed-loop stability and disturbance rejection properties via feedback control during the production cycle. Using two case studies, a CSTR and a polymerization reactor, we show that SBM-based scheduling has significant computational advantages compared to existing integrated scheduling and control formulations. Moreover, we show that the economic performance of our framework is comparable to that of existing approaches when a perfect process model is available, with the added benefit of superior robustness to plant-model mismatch.     

A study on scheduling of naphtha transportation and storage systems for naphtha cracking center

The rapid increase of petroleum prices compelled to petro-chemical industries to figure out ways to remove any potential redundancies in and out of their network. The increasing attention on integrating activities that have been addressed separately is in line with this trend. Particularly this paper addresses a naphtha feeding problem for Naphtha Cracking Center (NCC). The naphtha feeding problem involves two key operations: delivering naphtha from refineries to NCC and blending naphtha in storage tanks before feeding it to NCC. While the first is concerned with selecting sources and scheduling the loading and unloading of naphtha, the latter involves the transfer of the naphtha from storage tanks to a charging tank. This paper considers both issues simultaneously by transforming them into a single mixed linear integer programming problem of minimizing the cost function of naphtha prices, shipping expenses, and unloading costs, etc. A numerical example of a real industrial case is presented to illustrate the applicability of the proposed model.     

A reactive optimization strategy for the simultaneous planning,scheduling and control of short-period continuous reactors

The efficient and economic operation of processing systems ideally requires a simultaneous planning, scheduling and control framework. Even when the optimal simultaneous solution of this problem can result in large scale optimization problems, such a solution can represent economic advantages making feasible its computation using optimization decomposition and/or few operating scenarios. After reducing the complexity of the optimal simultaneous deterministic solution, it becomes feasible to take into account the effect of model and process uncertainties on the quality of the solution. In this work we consider those changes in product demands that take place once the process is already under continuous operation. Therefore, a reactive strategy is proposed to meet the new product demands. Based on an optimization formulation for handling the simultaneous planning, scheduling, and control problem of continuous reactors, we propose a heuristic strategy for dealing with unexpected events that may appear during operation of a plant. Such a strategy consists of the rescheduling of the products that remain to be manufactured after the given disturbance hits the process. Such reactive strategy for dealing with planning, scheduling and control problems under unforeseen events is tested using two continuous chemical reaction systems.     

A novel MILP formulation for short-term scheduling of multi-stage multi-product batch plants with sequence-dependent constraints

This paper presents a continuous-time mixed-integer linear programming (MILP) model for short-term scheduling of multi-stage multi-product batch plants. The model determines the optimal sequencing and the allocation of customer orders to non-identical processing units by minimizing the earliness and tardiness of order completion. This is a highly combinatorial problem, especially when sequence-dependent relations are considered such as the setup time between consecutive orders. A common approach to this scheduling problem relies on the application of tetra-index binary variables, i.e. (order, order, stage, unit) to represent all the combinations of order sequences and assignments to units in the various stages. This generates a huge number of binary variables and, as a consequence, much time is required for solutions. This paper proposes a novel formulation that replaces the tetra-index binary variables by one set of tri-index binary variables (order, order, stage) without losing the model's generality. By the elimination of the unit index, the new formulation requires considerably fewer binary variables, thus significantly shortening the solution time.     

Development Tendency of the Refractories for Iron ＆ Steel Industry in China

This paper introduces the developing tendency of refractories for iron and steel industry combined with the new metallurgical techniques applied in iron and steel industry in China. Refractories for blast furnace long service life, smelting reduction iron-making process, secondary refining metallurgies and clean steel making, near net shape continuous casting are described respectively. To meet the requirement of iron and steel industry, the high quality, multi-functions and environment-friendly refractories with long service llfe will be focused and developed in the future. In addition, high grade refractories in China have great developing space and potential market.