Feedback PID-like fuzzy controller for pH regulatory control near the equivalence point

In this research the use of a feedback PID-like fuzzy controller scheme for pH control is presented to deal with instability problems near the equivalence point in neutralization processes. State space analysis of the titration curves and a fuzzy clustering algorithm based on calculating a measure of potential derived from the square distance of the pH data are complementary applied to define the membership structure and the fuzzy sets of the controller. To test the performance of the controller, both simulated and experimental runs were used. The fuzzy controller was tested for compensating step-change perturbations of propionic acidic flow rates, propionic acid concentration, and buffering conditions. Stationary cycling behavior has been observed for large loads of acidic flow rates. It was found that though the rejection time was strongly dependent on the mean residence time of the liquid solutions, the proposed controller keep the neutralization process operating close to the specified set point of pH = 7.     

An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation

This paper presents a methodology for the application of receding horizon optimization techniques to the problem of optimally managing the energy flows in the chlor-alkali process using a hybrid renewable energy system (HRES). The HRES consists of solar and wind energy generation units and fuel cells to supply energy. The HRES is also connected to the grid and allows for buying or selling electricity from and to the grid. Initially, detailed models of each system component are introduced as the basis for the simulation study. Energy management strategies are then developed to realize the objectives of meeting production requirements while minimizing the overall operating and environmental costs. Sensitivity and uncertainty analyses are carried out to elucidate the key parameters that influence the energy management strategies. Finally, production demand response is integrated into the proposed methodology.     

Smart grid coordination in building HVAC systems: EMPC and the impact of forecasting

Energy consumption by heating, ventilation, and air conditioning (HVAC) systems exhibits a clear correlation with electricity prices. The method of economic model predictive control (EMPC) can be used in conjunction with thermal energy storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. Dynamic electricity pricing and weather condition forecasts can be readily incorporated within this methodology. Unfortunately, the receding horizon nature of this control strategy makes it very susceptible to the quality of the forecasts used. To this end, the development and implementation of several forecasting methods will be discussed. Finally, the EMPC performance of these methods will be assessed on a simple building example using active TES.     

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

Based on the structure of process models a hierarchically structured state-space model has been proposed for process networks with controlled mass convection and constant physico-chemical properties. Using the theory of cascade-connected nonlinear systems and the properties of Metzler and Hurwitz matrices it is shown that process systems with controlled mass convection and without sources or with stabilizing linear source terms are globally asymptotically stable. The hierarchically structured model gives rise to a distributed controller structure that is in agreement with the traditional hierarchical process control system structure where local controllers are used for mass inventory control and coordinating controllers are used for optimizing the system dynamics. The proposed distributed controller is illustrated on a simple non-isotherm jacketed chemical reactor.     

成品油调和建模及优化方法研究

炼油企业生产调度的优化因能提高资源利用效率、增加生产收率、满足需求和最大化利润而成为近年来的研究热点。本文以生产调度中的油品调和为例,分析和描述了调度过程建模所需的模型元素及其数学表达,其中涉及炼油企业典型的物料平衡及物性计算方法。针对生产操作规则对汽油和柴油的调和分别进行建模,求得在经济效益最大时各调和组分的质量以及主要性能指标,由此提出了一种针对成品油调和的优化方法。     

免疫粒子群算法在城市有轨电车运行调整中的应用研究

针对城市有轨电车运行环境复杂、列车运行调整存在众多约束条件、求解难度大等问题,提出虚站台概念,在此基础上建立了优化的列车运行调整模型.引入免疫系统中的免疫记忆特性和抗体浓度抑制思想,采用免疫粒子群算法对此模型进行求解,并以海珠区环岛新型城市有轨电车实验段数据进行仿真.结果表明,在引入虚站台概念基础上,采用免疫粒子群算法能够有效地解决城市有轨电车运行调整问题,并且其优化能力优于遗传算法.     

基于交叉突变算子的人工蜂群算法及其应用

人工蜂群（artificial bee colony,ABC）算法在解决多峰函数优化问题时经常会陷入局部最优,使得算法过早停滞,而在解决单峰问题时往往出现收敛速度过慢的问题。针对上述不足,为了进一步提高算法的优化性能,提出了一种基于交叉突变的人工蜂群（intersect mutation ABC,IMABC）算法。IMABC算法将整个蜂群依据其适应度值优劣进行划分,引入种群划分参数,对不同种群中的个体运用交叉突变算子,有效地平衡了种群的局部开采与全局探测能力,避免早熟收敛和提高收敛速度。从对基本函数的测试上可以看出,IMABC相对于GABC、IABC、ABC/best等改进的ABC算法,优化能力有了较大的提高。最后,将IMABC用于优化K-means算法,验证了该方法具有一定的实用性。     

多智能体入侵杂草算法

针对标准入侵杂草算法缺乏信息共享机制的缺陷,将多智能体系统融入标准入侵杂草算法,提出了一种新的多智能体入侵杂草算法.该算法通过多智能体系统中改进的邻域竞争合作算子实现个体间信息的交流,提高收敛速率;利用多智能体系统中的自学习算子增强算法求解精度.五个基准函数测试对比分析结果表明,多智能体入侵杂草算法的求解精度、收敛速度和稳定性优于标准入侵杂草算法、粒子群算法和差分进化算法.     

基于改进的简化粒子群聚类算法

针对粒子群算法后期收敛速度慢、易陷入局部极值的缺点,提出一种基于粒密度和最大距离积法的简 化粒子群聚类算法.通过采用线性递减与随机分布相结合的惯性权重策略、添加极值扰动算子、优化粒子个体最优位置,使粒子群算法能够快速收敛于全局最优.再把改进后的粒子群算法与K-means算法相结合,解决K-means算法因随机初始聚类中心而导致聚类效果差、不稳定等问题.通过实验分析,该算法的聚类结果准确率更高、收敛速度更快、稳定性更强.     

化学反应蚁群优化算法

针对化学反应优化对反馈信息利用不足导致后期求解效率低的问题,提出化学反应蚁群优化算法.该算法利用化学反应优化生成较优解,通过信息素转换策略将较优解转换为蚁群算法的初始信息素,最后由蚁群算法累积更新信息素得到最优解.以TSP为例进行仿真,结果表明,与化学反应优化、蚁群算法、模拟退火算法相比,所提算法具有更高的寻优能力、收敛效率和计算效率.