Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.

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。

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。

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.

将基于知识的规则模型和解析的数学模型有机结合，建立两级结构的实时专家控制系统（ECS）。由具有专家自学习功能的实时专家系统（ES）以专家优化方式寻找电解液最优酸锌含量比和最优温度，由实时控制系统（RTCS）控制新液流量、总地槽液位和冷却风机状态，保证生产过程在最优生产条件下进行。本文介绍了系统结构、专家优化技术、实时控制技术和系统实现。