Development of an expert system for on-line ventilation network analysis and graphic representation of mine ventilation parameters

The importance of ventilation analysis has increased in recent years as a means to determine the optimum ventilation design. The problem of ventilation is quite complicated due to a number of factors, such as increasing length and other complexities of airways, and difficult geological conditions. Therefore, it has been strongly felt that an intelligent ventilation analysis system, which could be applied to various situations to facilitate rapid and efficient operations in a mine, should be developed (Srivastava, et al., 1995; Sinha, 1996). A system is said to be intelligent if it has the capacity to learn (acquire knowledge), analyse things and take the appropriate decision. As such a Human Transparent Decision Support System (HTDSS) may be useful and could be made effective if the maximum possible data about the mine are collected on an on-line basis. For this, data are processed to extract information and, information is further processed to generate knowledge. Systems applying artificial intelligence in the simulation of decision making processes, utilizing knowledge as an ‘expert system’ or a ‘knowledge-based system’ are currently attracting considerable interest in a wide range of medical, financial and engineering applications and undoubtedly they have great potential in many areas of mining engineering (Briton, 1987; Kissel and King, 1988; Nutter et al, 1986; Rossouw, 1995). Expert systems are a class of computer programs that have the ability to analyse inputs and diagnose problems, and then recommend solutions. The expert system ‘ESVENT’ discussed herein derives the prediction of possible crises in the ventilation system from the available on-line ventilation data and suggests the relevant solution. The solution is reached after comparing its rule based in-built program with the database file and knowledge base made from the on-line ventilation data.