Prediction Of Raw Material Batches For The Production Of Clinker By Means Of Artificial Neural Networks -Analysis Of Behaviour

This research deals with the analysis of the behaviour of artificial neural nets for prediction of raw material batches for the production of clinker. During the production several oxides that are present in raw materials in quarries have to be extracted for homogenization of the mixture suitable for clinker production. There is some delay between the measurement of the mixture and the material which is send from quarry. It is necessary to “send” precise chemical composition to ensure a good quality of clinker and resulting product cement. Artificial neural networks (ANN) are suitable for such kind of timeindependent prediction. The results show that not all oxides are necessary to use for the prediction of one oxide. The ANN were designed into several nets with one input similarly as pseudo neural networks are able to work. The results will be used for the purpose of further research of pseudo neural nets which currently serve only as classifiers. INTRODUCTION The paper deals with the analysis of behaviour of artificial neural networks during the data training for prediction of raw materials batches for the production of clinker. This analysis should help for further research of pseudo neural networks. Pseudo neural networks were developed in 2013 and further tested (Kominkova Oplatkova et al. 2013a, 2013b, 2014). Pseudo neural networks (PNN) were inspired in artificial neural nets (ANN). There is synthesis of the model structure relation between inputs and outputs in both pseudo neural networks and artificial neural nets. ANN uses structures of nodes with transfer functions like sigmoid and training algorithms like back propagation or Levenberg-Marquardt. On the contrary, pseudo neural net synthesizes the structure via evolutionary process of symbolic regression there are no nodes and weights from the respective of ANN but there is a function dependent on inputs. The first simulations with PNN were connected with the area of classification. The principle of pseudo neural network allows usage of only one output but it can be used multiple inputs. Therefore classification into several classes was done in the way either of continuous intervals in one output node or connection of several independent nets (symbolically designed models) gives the combinations of values in more output nodes. As the process of pseudo neural networks is evolutionary driven some inputs were omitted in the final model. In some cases, it might serve as an advantage which reduces the complexity of the structure. And at the same time, the most important inputs connected with the problem are evolutionary selected. Tasks like prediction need values from the history and also historical data of other features and most probably all of them are necessary. As these values are given to the input layer, it might happen that PNN would omit some inputs. For the purpose of the further research with pseudo neural nets, the analysis of behaviour of standard multilayered perceptron (feedforward artificial neural net) was studied in this paper. As the dataset, data from the production of clinker was used. Researchers have explored some stages of the cement and clinker production with usage of ANN (Svinning et al. 2010, Stanisic et al. 2014, Topalov 2004) but they do not solve the prediction of the chemical composition of raw material. Firstly, introduction into the clinker production is given, than a description of artificial neural network (ANN) follows. Afterwards, the proposed experiment with results is explained. Conclusion and future work overview finish the paper. INTRODUCTION INTO CEMENT PRODUCTION Cement is the most common structural binder. It is a powdered inorganic hydraulic binder, which contains mainly a finely ground clinker. It is possible to affect its properties by means of selection, composition of raw materials and technological process of cement production and thus it can be predetermined to the widest possible usage or the specific usage. Nowadays, factories check workflow production and associated operations regularly and everything is subject to the maintenance and development of a functioning quality management system. Proceedings 29th European Conference on Modelling and Simulation ©ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors) ISBN: 978-0-9932440-0-1 / ISBN: 978-0-9932440-1-8 (CD) Cement production is very complicated, technically, time and energy consuming process, which comprises several stages: 1. Resource extraction. The crushed and milled material progresses through homogenization silos into stock silos. In this phase, regular analysis of chemical composition of mined rock are carried out. Based on analysis results, eg. the composition of raw materials or ground fineness can be adjusted. 2. Firing of clinker. The most important part of the cement production process. The raw powder passes through the heat exchanger, where the raw material is preheated and partial decarbonized, into the rotary kiln. During burning process artificial minerals are produced and by quenching they are stabilized and formed into Portland cement clinker. The most of the firing process is automatically controlled focusing on stability and little variability of resulting clinker parameters. 3. Grinding of clinker and storage. Clinker, together with other ingredients is ground to fine meal and then stored in silos. Relevant parameters are continuously checked. Testing is carried out on the modern equipment, such as a laser granulometer, automatic X-ray analyser and others. 4. Expedition. Cement is supplied either in bulk or packaged in paper bags. Prior to the shipment of cement is testing in accordance to technical standards. Modern control systems and the computer technology used in the cement production should guarantee its stable properties. An important contribution to the optimization, uniformity and control of the entire cement production process is automatic service laboratory. It is used for automated sampling at regular intervals, analyses and results archiving with minimizing of the human factor influence on inspection and test procedures during the cement production. The paper deals with the part of cement production which is aimed on optimal regulation (prediction) of the dosage of each mineral resource for the correct composition of clinker. PRODUCTION PROCESS The main raw materials for the production of cement are limestone and sodalite. These materials are mined in quarries with the help of blasting or only dragline using heavy equipment (Fig. 1). Wheel loaders and trucks transport the raw material into a crushing. Large stones are crushed to the size of the road gravel. Figure 1: Mining of the raw material in quarry and its crushing The crushed material with the help of a belt conveyor is transported to a materials landfill. The raw material is stored in the prehomogenized silo (Fig. 2) and then homogenized. Figure 2: Homogenization of the raw material Required mixture of crushed basic raw materials and correction of raw materials (silica sand, iron oxide correction) is prepared with the help of precision measuring equipment. Cylindrical or ball mills grind the raw material mixture to fine meal dry it at the same time. Then the meal is stored in silos and further homogenized (Fig. 3). Figure 3: Milling and drying of the raw material Firing of the raw materials needs temperatures about 1450 °C in an oven or in a rotary kiln with superior heat exchanger. These types of furnaces work in different techniques, while the main difference is the method of preparation and preheating the raw meal. The chemical reactions in a process known as sintering and subsequent quenching arises new product Portland cement clinker (Fig. 4). Clinker is the basic raw material for cement production. The production begins by grinding of input materials limestone, marl, clay shale, silica sand, fluorspar and iron ore. Raw materials are batched onto the conveyor belt, which is continuously analysed and the chemical composition of aggregates monitored continuously throughout volume and time. Dosing of the individual UTB ve Zlině, Fakulta aplikovane informatiky, 2010 13