Nowadays, decision-making is highly challenging in the alignment of decisions to support the success of plant performance and business goals. In this sense, enterprises comprise several functions which interact with each other, such as production, marketing, sales, human resources, logistics, safety and environment. Therefore, it is important to provide systems capable of representing and considering the different elements involved in the enterprise decision making task. In this area, knowledge management technologies have proved to be highly promising for improving information sharing and communication, enhancing the enterprise operation. The industrial waste management stands for an end-of-pipe problem involving gas, liquid or solid effluents which contain contaminants originated from industrial activities. Waste streams must meet discharge constraints imposed by environmental regulations before being disposed of in the environment. Thus, waste treatment plants comprise several technologies, and treatment allocation decisions are usually taken based on company-specific selection criteria. Therefore, there is a large amount of data and information which has to be collected and organized, and the choice of the adequate option for an entire manufacturing site with hundreds of continuously changing effluents becomes an overwhelming task. The use of an ontological model for representing a waste treatment plant has been detected as an opportunity for providing decision makers with new technologies to assess and evaluate the plant performance using information quality. This work aims at improving the information management based on a semantic representation of a waste treatment plant, namely its operational and logistics functions. As a result, more accurate information is provided to the optimization tools which will lead to better solutions based on the plant constraints.

Peer Reviewed