Stable pressure of gas collectors is beneficial to prolonging coke oven's life-time, consuming less energy and decreasing pollution. However, it is difficult to stabilize the pressure with conventional control methods because the collectors' pressure system is a timevarying, nonlinear multi-variable system that is strongly inter-coupled and disturbed. In this paper, a hybrid control approach that incorporates PID control withfeedforward control and expert control is presented. It integrates simplicity, reliability, flexibility and promptness in restraining disturbance by making full use of each method's advantages. The hybrid intelligent control system has been built to control the pressure of gas collectors. The results of actual runs show the effectiveness.

This paper describes an optimization prolem to minimize the cost of power consumption for the electrochemical porocess of zinc (EPZ) depending onvarying prices of electrical power. Ae series of conditional experiments was conducted to obtain enough data, whic reflect the complex relationships among the factors influecing power consumption. Two backpropagation neural networks are used to build a process model that describes these relationships. An equivalent Hopfield neural network is cnstructed to solve this nonlinear optimization poroblem with tcchnological constraints, a penalty function is introduced into the network energy function to meet the equality constraints, and inequality constraints are removed by altering the sigmoid function. An optimal power-dispatching control system (OPDCS) has been developed to provide an optimal power-dispatching scheme and keep the ePZ running economically. Since the OPDCS was put into service in a smeltery, the cost of power consumption has decreased significantly, and cost of power consumption has decreased significantly, and it also contrbutes to halancing the power grid load.

提出了一种混合实时任务容错调度算法。该算法采用Rate Monotonic（RM）算法完成周期任务的静态调度；采用预订处理机时间方法和Earlier Deadline First（EDF）算法动态调度非周期任务；采用主/副版本备份技术确保系统的容错能力。通过充分利用周期任务的剩余处理机时间调度非周期任务和主动备份与被动备份相结合的方法有效地减少了处理机数。仿真结果证明了算法的有效性。

焦炉配煤炼焦过程是存在诸多不确定性、无法用数学模型描述的复杂工业过程，传统控制方法难以实施控制。根据焦化理论和生产所获工业数据构造数学模型，以群体专家经验得到的定性知识构成规则模型，将解析的数学模型与基于知识的规则模型相结合，采用定性定量综合集成方法，建立焦炭质量预测模型，提出了配煤比计算的实用算法，并实时控制配煤流量。系统于1994年正式投入工厂运行，焦炭质量预测精度达到95%，配煤准确率达到97%。工业运行结果证明了方法的有效性。

本文从有效利用资源的角度出发，提出了一种以最小化处理机数目为优化目标的Best-Fit启发式容错调度算法。该算法采用主/副版本备份技术和副版本的主动运行方式与被运行方式相结合的方法，将实时任务的主版本和副版本调度到不同处理机上运行；并且按照Best-Fit启发式策略为实时任务主版本寻找“最佳满足”处理机，使尽可能多的实时任务副版本以被动方式运行。算法既保证了系统的实时性和容错性，也节约了处理机。分析和仿真结果均证明了算法的有效性。