An optimal power-dispatching system using neural networks for theelectrochemical process of zinc depending on varying prices ofelectricity

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.     

A distributed expert control system for a hydrometallurgical zinc process

A distributed expert control system (DECSHZ) has been built for a hydrometallurgical zinc process, whose basic steps are leaching, purification and electrolysis. It consists of a central computer management system and three local expert control systems, one for each of the basic steps. This paper deals with the design and application of DECSHZ, especially its distributed architecture and main functions; expert control strategies based on rule models and a combination of rule models and steady-state mathematical models; system implementation; and the results of actual runs. DECSHZ has been found to provide not only a very pure product, but also significant economic benefits.     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.     

An expert system for unit commitment and power demand prediction using fuzzy logic and neural networks

Abstract: The paper discusses the implementation of a fuzzy logic and artificial neural networks approach to providing a structural framework for the representation, manipulation and utilisation of data and information concerning prediction of power demand and generation commitments. An algorithm has been implemented and trained to predict the power demand at each load point on an hourly basis. The neural network is then implemented to supply the brute force necessary to accommodate the large amount of sensory data to provide the initial evaluation of the generation units to be committed. Results of the fuzzy model show a reasonable correspondence with the actual power demand. A standard deviation error for an hourly based prediction is limited to 4.4. Further refinement of the fuzzy model may produce further improvements.
Implementation of artificial neural networks for scheduling an hourly unit commitment based on load demands is also discussed The backpropagation technique based on the I/O mapping method has been chosen for structuring the neural network. Geographically related load points and generating units are clustered into groups. Grouping has significantly reduced the number of inputs and outputs to the neural network and, hence, reduced the system complexity. As a result, both training requirements and running real time interaction are significantly improved. The expert system would replace and utilise the requirement for skilled dispatchers in scheduling the generators. It is anticipated that this facility is more accurate, dynamic, adaptive and more efficient than a skilled dispatcher. The overall cost of power generation is expected to be less if the new facility is used. Initial results have reflected a satisfactory correlation between predicted and actual results, with a standard deviation error of 1.71% and 1.96% in the base load units of HTPS and ATPS respectively.     

An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks

Abstract: In this study a wavelet‐based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta‐bar‐delta, extended delta‐bar‐delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT‐BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.     

Synthetic neural networks for process control

Synthetic neural networks offer great promise for process control. A performance comparison is drawn between traditional statistical process control methods and neural networks. Specifically, a series of simulation experiments in which back propagation networks are contrasted with control charts is described. The basis for comparison is average run length (both predicted and observed) and accuracy. The Monte Carlo simulations are derived from plausible production process data. Neural networks were found to perform reasonably well under most conditions.     

Constructing neural networks from expert system rules

The implementation of a PC-based expert system rules-to-neural network translator is described. Knowledge expressed as rules is translated to a neural network representation. The generated structure simulates a neural network which is able to perform as the original expert system—conclusions are drawn from user-supplied facts based on the inherent knowledge.     

An expert system for process planning

Fully integrated manufacturing systems, the ultimate goal of today's production engineering research, cannot be realized without automated process planning systems. A knowledge-based expert system incorporating heuristic algorithms as well as analytical and empirical models and which can thus provide a partial replacement for human expertise is the only key to automated process planning.This paper presents an attempt to develop an expert system for automatic process planning using artificial intelligence. In this system, a structural database was incorporated with a knowledgebase for the execution of rules. The programs developed are user-friendly and highly interactive. They allow dialog between the system and the user and require only that the operator answer questions in a familiar workshop language.     

锌电解整流供电系统的微粒群优化控制策略

在分析锌电解整流供电系统的基础上,建立基于整流效率和整流系统稳流精度的整流供电系统优化控制数学模型, 并根据整流供电系统的分级递阶控制特性, 提出一种递阶多目标微粒群算法, 用于整流供电系统各机组电流分配的优化决策. 实验结果和现场运行结果都表明, 基于多目标微粒群算法的整流供电系统优化控制策略能够有效提高整流效率和稳流精度.     

Implementing expert system rule conditions by neural networks

The relation of subsymbolic (neural computing) and symbolic computing has been a topic of intense discussion. We address some of the drawbacks of current expert system technology and study the possibility of using neural computing principles to improve their competence. In this paper we focus on the problem of using neural networks to implement expert system rule conditions. Our approach allows symbolic inference engines to make direct use of complex sensory input via so called detector predicates. We also discuss the use of self organizing Kohonen networks as a means to determine those attributes (properties) of data that reflect meaningful statistical relationships in the expert system input space. This mechanism can be used to address the defficult problem of conceptual clustering of information. The concepts introduced are illustrated by two application examples: an automatic inspection system for circuit packs and an expert system for respiratory and anesthesia monitoring. The adopted approach differs from the earlier research on the use of neural networks as expert systems, where the only method to obtain knowledge is learning from training data. In our approach the synergy of rules and detector predicates combines the advantages of both worlds: it maintains the clarity of the rule-based knowledge representation at the higher reasoning levels without sacrificing the power of noise-tolerant pattern association offered by neural computing methods. This research is supported by Technology Development Center (TEKES) in Software Technology Programme (FINSOFT). Part of this work was done while the author was visiting AT & T Bell Laboratories.     

Optimal control for stochastic linear quadratic singular system using neural networks

In this paper, optimal control for stochastic linear singular system with quadratic performance is obtained using neural networks. The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the matrix Riccati differential equation (MRDE) obtained from well known traditional Runge–Kutta (RK) method and nontraditional neural network method. To obtain the optimal control, the solution of MRDE is computed by feed forward neural network (FFNN). Accuracy of the solution of the neural network approach to the problem is qualitatively better. The advantage of the proposed approach is that, once the network is trained, it allows instantaneous evaluation of solution at any desired number of points spending negligible computing time and memory. The computation time of the proposed method is shorter than the traditional RK method. An illustrative numerical example is presented for the proposed method.     

A hierarchical expert system for computer process control

This paper reports on the design and implementation of an expert system for computer process control (HESCPC). The complexity of the expertise necessary for computer process control applications requires that the expert system architecture be structured into a hierarchy of classes of specialized experts. The architecture of HESCPC integrates four classes of expert systems: operator/manager companion expert class, control system algorithm design expert class, hardware expert class, and software expert class. The paper is concerned with the design and implementation of the general system architecture, an operator adviser expert for a nuclear power plant and a control system designer expert using a state space feedback approach. Although the design and implementation aspects of all experts are discussed, the emphasis is on the latter.

At this stage of the HESCPC development, the declarative knowledge represented by 423 metarules and 1261 rules is distributed on a hierarchical structure among 20 experts on different levels of the hierarchy which are able to communicate among themselves to solve difficult control problems.

Examples of control system design sessions of linear mono and multivariable systems using feedback state space approach are given. A run time of an operator-adviser data-driven expert system for a nuclear plant is also presented.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

一种双种群协同进化算法在湿法炼锌过程中的应用

为了求解针铁矿法沉铁过程的多目标协调优化模型,从提高全局寻优能力和解的精度出发,提出一种基于改进全局搜索量子进化算法和局部搜索差分进化算法的双种群协同进化算法.数值仿真验证了该进化算法具有较好的收敛性和求解精度;典型工况的仿真优化结果表明了该多目标协调优化模型指导实际生产的可行性,以及所提出算法的有效性.     

An expert system for manufacturing cell control

An expert control system was designed to control an unmanned manufacturing cell in order to meet the operational requirements of a cellular Manufacturing System (CMS). In this paper, a knowledge-based three-layer control concept was used to build the cell control system. This cell control system is built to include workers' experience and problem handling ability. The cell control algorithms and heuristics are based on the pull system control principle. A Petri net is used to generate the cell control algorithm. The structure of the control system and the application of the Petri net method will be demonstrated.     

A review of neural networks for statistical process control

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.     

An adaptive system for process control using genetic algorithms

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GAs) are used to augment fuzzy logic controllers (FLCs). GAs are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLCs are rule based systems that efficiently manipulate a problem environment by modeling the “rule-of-thumb” strategy used in human decision making. Together, GAs and FLCs possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.     

Artificial neural networks in process estimation and control

In this contribution, the suitability of the artificial neural network methodology for solving some process engineering problems is discussed. First the concepts involved in the formulation of artificial neural networks are presented. Next the suitability of the technique to provide estimates of difficult to measure quality variables is demonstrated by application to industrial data. Measurements from established instruments are used as secondary variables for estimation of the “primary” quality variables. The advantage of using these estimates for feedback control is then demonstrated. The possibility of using neural network models directly within a model-based predictive control strategy is also considered, making use of an on-line optimization routine to determine the future inputs that will minimize the deviations between the desired and predicted outputs. Control is implemented in a receding horizon fashion. Application of the predictive controller to a nonlinear distillation system is used to indicate the potential of the neural network based control philosophy.     

Combining expert neural networks using reinforcement feedback for learning primitive grasping behavior

This paper present an architecture for combining a mixture of experts. The architecture has two unique features: 1) it assumes no prior knowledge of the size or structure of the mixture and allows the number of experts to dynamically expand during training, and 2) reinforcement feedback is used to guide the combining/expansion operation. The architecture is particularly suitable for applications when there is a need to approximate a many-to-many mapping. An example of such a problem is the task of training a robot to grasp arbitrarily shaped objects. This task requires the approximation of a many-to-many mapping, since various configurations can be used to grasp an object, and several objects can share the same grasping configuration. Experiments in a simulated environment using a 28-object database showed how the algorithm dynamically combined and expanded a mixture of neural networks to achieve the learning task. The paper also presents a comparison with two other nonlearning approaches.     

混沌系统的混合遗传神经网络控制

在小扰动控制技术基础上,将暂态误差预测方法和遗传算法结合起来,提出了一种混合遗传神经网络控制非线性混沌系统的新方法(简称HyGANN).通过增强学习训练,HyGANN可产生控制混沌状态的小扰动时间序列信号,Henon映射的计算机仿真结果表明,它不仅有效镇定混沌周期1,2等低周期轨道,还可成功将高周期混轨道变成期望周期行为.