Construction industry is an important and dynamic sector in the world economy. It is responsible for the creation of jobs, the modernization of infrastructure and the improvement of life standards, among others. Also, this industrial sector is directly related to many other areas, such as financing, energy, mining and transportation, just to mention a few. Cement, the most important component of construction, plays a fundamental role in modern world. This material is produced in almost every country and region and presents many interesting aspects, such as physical, chemical and mechanical stability. Cement, when compared to other materials, can be considered inexpensive and abundant. On the other hand, in many aspects, the chemistry of cements is not so familiar or well established. Additionally, the production of cement is not an environmentally friendly process. It requires very high temperatures (usually above 1,500C) and the consumption of large amounts of non-renewable raw materials. Numbers related to cement-based materials and constructions are very impressive. Engineers use to say that concrete is the second component mostly used by man, just after water. Cement, as a technological material, is very successful, as everyone knows. Many characteristics can be easily cited. First of all, it works very well at room temperature. It is simple to use, easy to shape, and within few hours, renders an ‘‘artificial’’ rock, having numberless applications. Cement is used to build simple houses, highways, bridges and more complex systems such as dams and nuclear power plants. In fact, it is almost impossible to imagine the world without cement or concrete [1]. All these uses of this product can lead to an increase in energy use, so the integration of artificial intelligence techniques will be useful for early estimation of energy and cement flow to reduce operating costs and ensure energy optimization.
However, due to the large number of input process parameters involved in cement manufacturing, it is very difficult to assess the impact of each process parameter on energy and throughput. In the cement industry, the majority of the methods for evaluating the output parameters of the kiln process are done manually by the people responsible for the process who have previous experience. The main goal behind this was to increase the throughput while decreasing the energy, it was difficult to maintain both conditions.
Recently, various researchers have used artificial intelligence techniques such as artificial neural networks (ANN), fuzzy logic and genetic algorithms to predict the output parameters of the process [2].