抗生素发酵过程优化调度模型的研究

对抗生素多罐并行发酵过程进行了分析,将任务、设备和事件之间的分配关系表达为两类0-1变量,建立了一个基于连续时间的抗生素多罐并行发酵过程优化调度的M ILP(混合整数线性规划)模型。该模型整数变量少,求解速度快,并给出了最短生产时间和罐批最优生产序列。最后以头孢菌素发酵过程为例证明了此调度模型的可行性和有效性。     

基于状态设备网络的改进间歇生产调度模型

建立有效的间歇生产调度模型一直是生产调度问题研究的热点,基于特定事件点的连续时间建模方法是优化短期间歇生产调度问题的有效工具。基于状态设备网络和特定事件点概念,建立非线性的连续时间间歇生产调度模型。为了解决非线性引起的求解困难,该模型使用替代方法线性化模型中的双线性项,替代法不仅将建立的混合整数非线性规划模型转化为混合整数线性规划模型,且由于其不包含大M松弛项,能使模型搜索空间更紧凑,模型求解效率更高。通过3个实例对比实验表明了基于状态设备网络描述的改进间歇生产调度模型搜索高效性。另外,模型中还给出了不同存储条件下,基于状态设备网络描述的间歇生产调度模型约束,扩展了模型适用性。     

基于状态设备网络的改进间歇生产调度模型

建立有效的间歇生产调度模型一直是生产调度问题研究的热点,基于特定事件点的连续时间建模方法是优化短期间歇生产调度问题的有效工具。基于状态设备网络和特定事件点概念,建立非线性的连续时间间歇生产调度模型。为了解决非线性引起的求解困难,该模型使用替代方法线性化模型中的双线性项,替代法不仅将建立的混合整数非线性规划模型转化为混合整数线性规划模型,且由于其不包含大M松弛项,能使模型搜索空间更紧凑,模型求解效率更高。通过3个实例对比实验表明了基于状态设备网络描述的改进间歇生产调度模型搜索高效性。另外,模型中还给出了不同存储条件下,基于状态设备网络描述的间歇生产调度模型约束,扩展了模型适用性。     

考虑调度操作安全平稳性的炼油化工生产过程调度优化

平稳运行是炼油化工企业本质安全运行、生产经济效益和挖潜增效的重要保证，然而由于原油供应以及产品需求多变，炼油化工生产装置的多操作模式运行已成为普遍现象，调度调整也日渐频繁。现有的研究方法未考虑到装置多模式切换等调度调整对平稳操作带来的影响，甚至会导致调度操作方案实际不可行。为此，提出一种考虑调度操作平稳性的炼油化工生产调度优化模型，以解决常规调度优化模型优化产生的调度调整易使生产波动从而导致调度方案不可行的难题。考虑调度操作平稳性的调度优化模型采用离散时间表示，通过操作模式的切换与装置加工速率的波动综合表征系统平稳性，建立以生产成本最低为目标的调度优化模型。为了验证所提出模型在解决实际工业问题时的有效性，采用Julia的JuMP包调用Gurobi求解器对典型案例进行仿真求解。案例仿真结果验证了所提出模型的正确性及可实施性。与常规调度优化相比，以过程动态为表征的调度操作的平稳性提高了10%以上。     

多产品多级连续化工过程生产调度的研究

对多产品多级连续化工过程的生产调度问题进行了研究,考虑了设备清洗和设备生产效率下降对生产过程的影响.依据该过程具有组合和连续性特点,将该调度模型归结为一混合整数非线性规划问题(MINLP),以利润最大为目标,采用改进的遗传算法对该模型进行了求解.用一个三产品两级连续过程的例子说明了该算法的有效性.     

一种改进的顺序型多目的间歇工厂生产调度的MILP模型

分析了顺序型多目的工厂间歇生产的特点,建立了一种新的混合整数线性规划（MILP）模型.该模型以操作活动序列作为建模的依据,将整个间歇生产过程分解成若干个子系统.在对各子系统建模的基础上,实现了顺序型多目的工厂间歇调度的全局性优化.并通过算例验证了该模型求解结果的正确性和可行性.然后将新模型运用到更大规模的调度问题上,均求解出了有效的调度方案.     

基于遗传算法的汽油调和生产优化研究

油品调和调度问题可归结为混合整数非线性规划 (MINLP)问题。针对汽油调和生产调度选取了预测汽油辛烷值和蒸汽压的合适方法,提出了基于逻辑的数学模型,采用改进的遗传算法进行求解。将其应用于生产实例,通过与文献结果比较验证了该方法的有效性。     

多目的间歇化工过程最优设计——SA/LP算法

提出了具有多条生产路线的多目的间歇化工过程最优设计混合整数非线性规划(MINLP)模型,该模型允许同时设立同步、异步平行单元以及中间储罐,并允许设备尺寸离散变化.在结合模拟退火(SA)和线性规划(LP)的基础上提出了可求解上述MINLP问题的SA/LP算法,该算法结合了SA全局收敛性好和LP可处理连续变量与约束方程的优点.计算表明,上述模型与算法实施简便,得到了文献算例中未得到的全局最优解,且在计算速度、内存占用上都远远优于文献中的方法.     

ZW多产品间歇过程调度及在线调整

针对多产品间歇过程调度提出了分层递阶的Petri网建模方法，利用赋时Petri和Petri网的简化技术描述不同层次的生产问题，具有很强的模型描述能力。另外，在调度决策层可以方便地集成优化策略和在线调整算法，使问题求解更加灵活。     

分布式并行算法在长周期原油混输调度中的应用

为了有效求解长周期原油混输调度问题,提出了基于事件树的分布式并行算法。该方法把原油混输调度问题分解为码头调度子问题和厂区调度子问题,采用基于事件树的建模方法,并根据两个子问题的求解顺序提出了原油混输调度问题的分布式并行算法。本方法采用主从式并行结构,主节点把求解码头调度子问题所需的原油质量要求信息发送到各从节点,然后各从节点把与质量要求信息对应的码头调度最优解返回给主节点,通过综合比较两个子问题的解,从而得出最优的调度方案。实例计算表明,该并行算法可以有效减少问题的求解时间,特别是对不同常减压对原油质量要求不同时的长周期调度（如4周）问题,采用串行算法在48 h内都无法得到可行解,而采用此算法用3台计算机可以在25 h内得到最优解。     

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.     

Scheduling Model for a Multi‐Product Batch Plant Using a Pre‐Ordering Approach

A scheduling model for a multi‐product, multistage batch plant with parallel units is presented. The objective is to maximize the weighted completion times of orders in every processing stage while imposing a penalty on the slower orders. The proposed model uses the continuous‐time representation mode and describes the allocations of tasks, units and stages by a set of binary variables. In order to reduce the model size and provide a more effective solution to the model, a pre‐ordering approach that sorts the processing sequence of orders is developed. The pre‐ordering approach identifies the infeasible assignments through which the number of binary variables is significantly reduced. Illustrative examples are provided to show that the size of the proposed model is small, and therefore, needs much less computational effort in comparison with the existing models in the literature.     

Parallel optimization by means of a Spectral-Projected-Gradient approach

The judicious exploitation of the inherent optimization capabilities of the Spectral-Projected-Gradient method (SPG) is proposed. SPG was implemented in order to achieve efficiency. The novel adjustments of the standard SPG algorithm showed that the parallel approach proves to be useful for optimization problems related to process systems engineering. Efficiency was achieved without having to relax the problems because the original model solutions were obtained in reasonable time.     

并联连续再生/再循环的多杂质间歇用水系统优化方法

为了有效地减少多杂质间歇用水系统的新鲜水消耗量和再生处理量,针对多杂质间歇过程用水网络提出连续操作并联再生处理单元水网络结构模型及其优化设计方法。通过在用水网络中设置中间储罐和再生单元以实现对不同水质废水并联分质处理,建立了减少用水系统的新鲜水用量和再生水量以及废水排放量的数学规划模型,采用GAMS软件对一个实例进行求解。计算和分析表明：提出的水网络结构与优化设计方法可有效地解决按水质对废水进行并联处理的间歇用水系统,使系统的新鲜水用量和再生水流率同时达到最小。     

Optimal design of multiproduct batch plants considering duplication of units in series

A generalized disjunctive programming (GDP) model for the optimal design of multiproduct batch plants is presented. This general model manages the duplication of units in series to perform a given operation in the process, which is an alternative that has not been considered in previous general approaches. Unlike duplication in parallel, duplication in series is only applicable to some operations which present trade-offs between duplication and other cost-impacting elements in the batch process. In order to use a fixed time and size factor model some assumptions had to be made in the operations that allow the duplication in series. To show the effectiveness of this approach, a plant that produces multiple recombinant proteins is presented and solved.     

SCHEDULING OF CONTINUOUS PARALLEL LINES IN THE EVAPORATION SECTION OF SUGAR PLANTS

Cost reduction in sugar industries can be achieved just by adjusting and optimizing structures and/or key process variables to assure maximum use of the sugar formed in the field. The evaporation section is the critical stage for efficient management of water and energy resources. Thus, the selected case study addresses the optimization of the operation of continuous evaporation units working in parallel and presenting a decrease of in efficiency with time. The problem was solved through mathematical programming that obtained optimum values for the operating variables. Therefore, it was necessary to mathematically formulate the optimization problem and to model the operations under study. A mixed integer nonlinear programming model (MINLP) was used in order to meet the targets. Also, a technical objective function with economical background was developed. The industrial implementation required as well the conception and development of a user-friendly decision-making tool to facilitate the routine work and to allow plant managers to check, accept, and put into practice the solution proposed. The application of the presented strategies to an industrial case study demonstrates the efficiency and potential of the approach adopted.     

Disaggregation-aggregation based model reduction for refinery-wide optimization

In this paper, reduced nonlinear refinery models are developed by generating and using input-output data from a process simulator. In particular, rigorous process models of continuous catalytic reformer (CCR) and naphtha splitter units are used for generating the data. To deal with complexity associated with large amounts of data, that is usually available in the refineries, a disaggregation-aggregation based approach is presented. The data is split (disaggregation) into smaller subsets and reduced artificial neural network (ANN) models are obtained for each of the subset. These ANN models are then combined (aggregation) to obtain an ANN model which represents all the data originally generated. The disaggregation step can be carried out within a parallel computing platform. Refinery optimization studies are carried out to demonstrate the applicability and the usefulness of the proposed model reduction approach.     

Optimal scheduling of continuous plants with energy constraints

This work addresses the scheduling of continuous single stage multiproduct plants with parallel units and shared storage tanks. Processing tasks are energy intensive and we consider time-dependent electricity pricing and availability together with multiple intermediate due dates, handled as hard constraints. A new discrete-time aggregate formulation is proposed to rapidly plan the production levels. It is combined with a continuous-time model for detailed scheduling as the essential part of a rolling-horizon algorithm. Their computational performance is compared to traditional discrete and continuous-time full-space formulations with all models relying on the Resource-Task Network (RTN) process representation. The results show that the new models and algorithm can generate global optimal schedules much more efficiently than their counterparts in problems involving unlimited power availability. Under restricted power, the aggregate model underestimates the electricity cost, which may cause the rolling-horizon approach to converge to a suboptimal solution, becoming the discrete-time model a better approach.     

竖炉燃烧过程智能故障预报系统

赤铁矿竖炉燃烧过程机理复杂,运行工况变化频繁,使得故障易发,从而导致生产不稳定。将案例推理和软测量技术相结合,提出一种竖炉燃烧过程的智能故障预报方法。软测量模型对难以在线测量的关键工艺参数进行实时测量,基于案例检索与重用的故障预报模型根据过程数据及关键工艺参数软测量值的变化对燃烧过程的典型故障进行趋势预报,采用概率的形式表达诊断结果,并提供操作指导,可以有效避免故障的发生。将建立的故障预报系统应用于竖炉燃烧过程的生产实际中,故障发生率明显降低,表明了方法的有效性。     

On the fuzzy logic for rule based modeling and process simulation

Some chemical processes are not well understood, and not easy to analyze and simulate with conventional mathematical approaches. A rule based approach is proposed in this paper to model such process units. Conversion between fuzzy and crisp numbers at the input and output of these units is discussed in details and a slicing conversion approach is proposed. It is shown from a case study that the approach provides a way to incorporate fuzzy models into conventional process simulators.