The major health hazards from smoke and dust are due to microscopic fine particles present in smoke as well as in dust. These fine particles, which are microscopic in nature, can penetrate into human lungs and give rise to a range of health problems such as irritation in eyes, a runny nose, throat infection, and chronic cardiac and lung diseases. There is a need to device such mechanisms that can monitor smoke in thermal power plants for timely control of smoke that can pollute air and affects adversely the people living nearby the plants. In order to solve the problems of low accuracy of monitoring results and long monitoring time in conventional methods, a real-time smoke and dust monitoring system in thermal power plants is proposed, which makes use of modified genetic algorithm (GA). The collection and calibration of various monitoring parameters are accomplished through sampling control. The smoke and dust emission real-time monitoring subsystems are employed for the monitoring in an accurate manner. A dual-channel TCP/IP protocol is used between remote and local controlling modules for secure and speedy communication of the system. The generic GA is improved on the basis of the problem statement, and the linear programming model is used to avoid the defect of code duplication with genetic operations. The experimental results show that the proposed smoke and dust monitoring system can effectively improve the accuracy of the monitoring results and also reduce the time complexity by providing solutions in a faster manner. The significance of the proposed technique is to provide a reliable basis for the smoke and dust emission control of thermal power plants for safeguarding the human health.

Bayesian Network has an advantage in dealing with uncertainty. But It is difficult to construct a scientific and rational Bayesian Network model in practice application. In order to solve this problem, a novel method for constructing Bayesian Network by integrating Failure Mode and Effect Analysis(FMEA) with Fault Tree Analysis(FTA) was proposed. Firstly, the structure matrix representations of FMEA, FTA and Bayesian Network were shown and a structure matrix integration algorithm was explained. Then, an approach for constructing Bayesian Network by obtaining information on node, structure and parameter from FMEA and FTA based on structure matrix was put forward. Finally, in order to verify the feasibility of the method, an illustrative example was given. This method can simplify the modeling process and improve the modeling efficiency for constructing Bayesian Network and promote the application of Bayesian Network in the system reliability and safety analysis.

Scheduling is one of the core links of modem industrial production. Scheduling needs to be designed according to the characteristics of the production line. In order to optimise the problem of workshop scheduling, the service-oriented programming idea and advanced technology to optimise the system development of mixed flow shop are adopted. The system is designed for applications in a distributed network environment. In this paper, an improved heuristic industrial production scheduling method is proposed to solve the scheduling system problem with multiple scheduling tasks, multiple processes, multiple stations, multiple constraints and rules. This method specifies the processing equipment, start tim.eand completion time for each process of the production task. The application methadshows that the proposed method can improve the automation and intelligence level of the scheduling process, improve the utilisation rate of equipment and other production resources, and give full play to the enterprise production capacity.

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