补料分批发酵过程优化控制

针对非线性、时变的发酵过程，建立了神经网络模型进行菌体浓度、基质糖浓度和产物浓度的在线估计。采用神经网络非线性预测控制方法，结合遗传算法寻优技术确定发酵过程的优化轨线，通过在线调整实现对优化轨线的跟踪控制。     

补料分批发酵过程优化软件平台的设计

设计了基于DDE的VB、Exccl和Matlab的发酵过程优化软件平台，并将该软件平台应用到多粘菌素发酵过程pH值的寻优。实践表明该优化软件平台能够确定发酵过程被控参数的优化轨线，为优化控制提供一个主要目标。     

神经网络、遗传算法在发酵优化软件平台设计中的应用

本课题设计了基于DDE的VB、Excel和Matlab的发酵过程优化软件平台，并将该软件平台应用到多粘菌素发酵过程PH值的寻优。实践表明：该优化软件平台能够确定发酵过程被控参数的优化轨线，为优化控制提供一个主要目标。     

发酵过程混合建模与优化控制新方法

针对当前微生物发酵过程存在因为生物传感器不具备足够的准确性和灵敏性,实验时的菌液和产物浓度等生化指标难以实时监测和控制等缺点,提出了采用量子粒子群优化算法(QPSO)优化最小二乘支持向量机(LSSVM)参数的QPSO-LSSVM混合建模新方法,并用于多粘菌素的发酵过程建模;同时,基于此模型,采用QPSO算法对pH值与溶解氧浓度Do控制轨线进行优化研究;首先,利用LSSVM进行发酵过程的建模,然后采用QPSO对LSSVM建模过程中的重要参数进行优化调整,形成QPSO-LSSVM混合建模与优化控制方法;仿真结果表明,该方法得到的模型能取得更好的预测效果,优化后的pH值与Do浓度控制轨线能够提高最终的产物浓度;该方法用于发酵过程的建模和重要参数的优化控制是可行的、有效的。     

基于MOGA和SVM的发酵过程建模

针对微生物发酵过程，使用多目标遗传算法（multi-objective genetic algorithm, MOGA）确定最优参数．MOGA和SVM回归相结合形成一种新的建模方法，该方法利用现场生产数据建立了青霉素效价预估模型．仿真结果表明此方法具有很强的拟合和泛化能力．MOGA方法的有效性也得到了验证，它也能够自动选择最优参数．     

一种改进的遗传算法及其在PID控制中的应用

针对经典遗传算法收敛速度慢、易于早熟、局部寻优能力差等缺点，提出了一种改进的遗传算法，并将其应用于PID参数寻优。该算法既具有经典遗传算法的全局寻优能力，又具有局部寻优能力；同时，它又能有效地抑制早熟，保证得到的优化参数为最优。仿真结果表明，基于此遗传算法寻优设计的PID控制器可以极大地提高寻优的速度，鲁棒性强，具有很好的动态品质和稳定性。     

发酵过程建模与优化控制的研究

针对发酵过程的时变性和强非线性,本研究采用神经网络对发酵过程建模,在线预测某些重要生物参数,并以这些生物参数的预测值为指导,使用遗传算法(GA)在线优化主要的环境参数,确定各参数的最优轨线来控制发酵过程,并构建了由PC机和单片机组成的发酵优化控制的监控系统.实验表明,该控制系统应用于实际发酵生产过程能有效提高发酵生产效率.     

多粘菌素补料分批发酵过程pH值的优化控制

针对补料分批发酵过程pH值优化轨迹难以确定和控制的问题,为了获取pH值的优化轨迹并加以有效控制,利用神经网络非线性预测控制方法结合遗传算法寻优设计基于动态数据交换的VB,Excel和Matlab的发酵过程优化软件平台;通过该优化软件平台找到多粘菌素发酵过程pH值的优化轨迹;对它采用参数自调整模糊控制方法,使之按优化轨线变化.统计多粘菌素10批次优化控制发酵生产结果,发酵时间平均缩短5 %,提高产量3 %.     

基于神经网络-遗传算法的双轴运动系统PID控制

提出了一种针对双轴运动系统的基于神经网络-遗传算法的PID控制器参数寻优设计方案。离线部分用遗传算法(GA)的寻优得到一组最优的PID参数Kp^ ，Ki^ ，Kd^ ，并将其作为存线调整部分的仞始值；在线部分用神经网络的BP网络调整系统的瞬态PID响应，同时利用插补器使双轴运动系统进行圆弧插补运动。通过计算机仿真可证明，此寻优方法具有良好的控制性能。     

微生物发酵动力学模型及其参数拟合的软件实现

发酵动力学主要研究发酵过程中菌体生长、产物合成和底物消耗之间的关系,对发酵过程的调控及发酵规模的放大都有着重要的指导意义.目前微生物发酵动力学一般由菌体生长动力学、产物生成动力学和底物消耗动力学三部分组成,但是其模型绝大多数都是非线性,参数拟合难度大.目前常用的估算方法有线性转化拟合、非线性拟合和遗传算法拟合法.本文首先总结目前国内外分批发酵中常用的数学模型及其表达式,然后通过实例并结合软件编程详细的介绍了这3种方法的软件实现方法,并且比较3种方法拟合效果.结果表明线性转化法拟合误差较大,非线性和遗传算法拟合效果较好,但遗传算法能以较大概率逼近全局最优,而非线性拟合法则容易陷入局部最优.     

Operation optimization of hydrocracking process based on Kriging surrogate model

Hydrocracking is one of the key technologies in oil refining. It has become a critical secondary processing unit in the refinery for improving the quality of product oil and increasing the light oil volume of production. As such, operation optimization for this process is significant. The basis of operation optimization is the model, and several mechanisms for hydrocracking models have been proposed and studied. However, these models usually require time consuming and exhibit low efficiency especially when applied to optimize operating conditions. In this study, a Kriging surrogate model of hydrocracking is developed based on the mechanism and industrial data. An optimization algorithm is then proposed to optimize operating conditions. The proposed algorithm integrates adaptive step-size global and local search strategy (GLSS) for minimizing the predictor. Simulation results indicate that this optimization strategy integrating GLSS and Kriging surrogate model obtains better revenue of the process production than conventional algorithms such as EGO, DDS, and CAND.     

Optimal process design of two-stage multiple responses grinding processes using desirability functions and metaheuristic technique

Two-stage grinding processes in mass-scale manufacturing unit are usually too complex to optimize, due to large number of interacting process variables, between and within the stages. Furthermore, statistical design of experiment techniques, such as factorial design, fractional factorial and response surface design by sequential experimentations, to determine the exact optimal process design for the overall interdependent two-stage system, are sometimes too difficult to implement, if not impossible. In this context, considering each stage in isolation and determining individual optimal conditions may not result in an optimal process design, when the entire two-stage system is considered. The aim of this study is to apply empirical modelling technique based on direct observations, for prediction of a two-stage grinding process behaviour having multiple response characteristics of continuous variables, and determine overall optimal process design to meet the specific customer requirements. In order to achieve the above goal, the study proposes an integrated approach using multivariate regression, desirability function, and metaheuristic search technique. Three different metaheuristic search techniques, viz. real-coded genetic algorithm, simulated annealing, and a modified Tabu search based on novel Mahalanobis multivariate distance approach to identify Tabu moves, are employed to determining near optimal path conditions for an industrial case study of two-stage CNC grinding (honing) optimization problem, having various process and variable constraints. Computational study results based on different metaheuristics, and applied on the same two-stage optimization problem, show that the modified Tabu search performs better and also offer opportunities to be extended for other multi-stage metal-cutting process optimization problems.     

保留精英遗传算法收敛性和收敛速度的鞅方法分析

论文引入鞅方法取代传统的马尔科夫链理论,研究保留精英遗传算法(EGA)的收敛条件和收敛速度.通过把EGA的最大适应值函数过程描述为下鞅,基于下鞅收敛定理构造使算法满足几乎处处收敛的充分条件,分析了概率1收敛充分条件与算法操作参数的关系,并计算了EGA获得全局最优解所需的最大进化代数.使用鞅方法分析遗传算法收敛性具有独特的优势,成为分析遗传算法收敛性及其性能的新方法.     

炼油企业生产计划与重点装置工艺条件集成优化

传统的生产计划优化由于不考虑过程装置的操作优化,从而无法保证企业生产计划层与过程操作层的全局最优.为了在获得炼油企业最优生产计划的同时,确保计划优化中重点装置的操作条件可以实现,本文建立了集成装置工艺条件的炼油企业生产计划优化模型.该模型引入常减压装置侧线产品切割点温度、催化裂化装置转化率等过程工艺条件,基于物料质量平衡、产品质量指标约束等关系,进行厂级生产计划建模与求解,确定可达的装置操作条件.应用案例中重点通过与传统常减压装置侧线收率固定的生产计划方案比较,证明在满足可达的操作条件下,集成装置工艺条件操作范围的生产计划优化模型,可以实现更高的全厂利润与更优的装置收率分布,同时优化结果对炼厂实际生产更具有指导意义.     

Robust MPC for systems with output feedback and input saturation

In this work, it is proposed an MPC control algorithm with proved robust stability for systems with model uncertainty and output feedback. It is assumed that the operating strategy is such that system inputs may become saturated at transient or steady state. The developed strategy aims at the case in which the controller performs in the output-tracking scheme following an optimal set point that is provided by an upper optimization layer of the plant control structure. In this case, the optimal operating point usually lies at the boundary of the region where the input is defined. Assuming that the system remains stabilizable in the presence of input saturation, the design of the robust controller is performed off-line and an on-line implementation strategy is proposed. At each sampling step, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. Stability of the closed-loop system is forced by considering in the off-line step of the controller design, a state contracting restriction for the closed-loop system. To produce an offset free controller and to attend the case of unknown steady state, the method is developed for a state-space model in the incremental form. The method is illustrated with simulation examples extracted from the process industry.     

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.     

Data-based multiple-model prediction of the production rate for hematite ore beneficiation process

The prediction of the production rate of the hematite ore beneficiation process is important to plant-wide optimization. This paper presents a data-based multi-model approach to predict the production rate with multiple operating modes. The inputs of the predictive model are the performance indices of each unit process, and the output is the global production index (the production rate) of the hematite ore beneficiation process. The multiple models are developed by integrating the fuzzy clustering algorithm and machine learning algorithm. A global model, Takagi–Sugeno–Kang fuzzy model, and multiple neural network model were compared using the data obtained from a practical industrial process, and the effectiveness of the proposed algorithm was proven.     

Machining process sequencing with fuzzy expert system and genetic algorithms

Traditional process planning systems are usually established in a deterministic framework that can only deal with precise information. However, in a practical manufacturing environment, decision making frequently involves uncertain and imprecise information. This paper describes a fuzzy approach for solving the process selection and sequencing problem under uncertainty. The proposed approach comprises a two-stage process for machining process selection and sequencing. The two stages are called intra-feature planning and inter-feature planning, respectively. According to the feature precedence relationship of a machined part, the intra-feature planning module generates a local optimal operation sequence for each feature element. This is based on a fuzzy expert system incorporated with genetic algorithms for machining cost optimization according to the cost-tolerance relationship. Manufacturing resources such as machines, tools, and fixtures are allocated to each selected operation to form an Operation-Machine-Tool (OMT) unit in the manufacturing resources allocation module. Finally, inter-feature planning generates a global OMT sequence. A genetic algorithm with fuzzy numbers and fuzzy arithmetic is developed to solve this global sequencing problem.