Prediction of critical parameters for automation of kiln process using DNN regression

Abstract

Cement kiln, the most energy consuming unit of a cement factory, carries out the clinker manufacturing process, which must be operational with stable conditions to achieve consistent clinker quality and maximum production rate. In order to maintain smooth and stable conditions inside the rotary kiln system (RKS), some process control parameters should vary within their desired ranges. This is achieved by doing some adjustments to the kiln control variables. In most of the cement plants, this overall control can only be achieved by manual control by operators. The physicochemical and thermochemical reactions of the RKSs are not yet well understood due to their complexity. Therefore, the behavioral patterns inside the kiln cannot be determined exactly by the operators. Sometimes they end up with wrong decisions for control variables, which can cause the RKS to become unstable and cause huge losses to the cement company. Few automation research studies have been conducted for continuous prediction of control variables for kiln process. However, not all of them address the actual inefficiencies that occur in processes, equipment, and the entire system by recognizing kiln behavioral patterns. Therefore, the automation of clinker production processes with proper prediction model is necessary and it helps to increase production, improve product quality, reduce production costs and operator interventions. This research study is to predict critical control variables such as fuel rate, kiln speed and waste gas fan speed for given RKS parameters to maintain desired process condition inside the RKS. The RKS of Siam City Cement Lanka Limited is used as the case study. A regression based DDN model is implemented and trained for the best accuracy by adjusting hyperparameters. Model evaluation is done until obtaining a minimum error. The results of the model validation in real time scenario are also presented and discussed.