Hua Yan

In this paper, we consider optimal production and setup scheduling in a failure-prone manufacturing system consisting of a single machine. The system can produce several types-of products, but at any given time it can only produce one type of product. A setup is required if production is to be switched from one type of product to another. The decision variables are a sequence of setups and a production plan. The objective of the problem is to minimize the cost of setup, production, and surplus. An approximate optimality condition is given together with a computational algorithm for solving the optimal control problem.

Non-invasive techniques such as electrical capacitance tomography (ECT) are beginning to make promising contributions to control systems and are well fitted for flow-regime identification in opaque pipes or conduits. A new method of two-component flow-regime identification based on a neural network and an eight-electrode ECT sensor is proposed in this paper. Time-consuming image reconstruction and analysis are avoided. Ten feature parameters are extracted straight from the capacitance measurements and translated into regime information via a back-propagation (BP) network. The extraction of feature parameters, the architecture and the training of the BP network are given. Simulation results show that the new identification method has good precision and high speed. The use of feature parameters and the BP network for flow-regime identification is promising.