A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract

Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

“…Artificial intelligence (AI) technologies and machine learning (ML) algorithms have been increasingly utilized to solve hard problems that inherit nondeterminism and randomness in engineering practice of a variety of fields including complex system control [19,20], object detection [21], communication networks [22], oil pipeline monitoring [23] and leak detection [24], industrial control system protection [25], waste material recycling [26], etc. In recent years, more and more AI/ML practitioners collaborate with experts in different industrial domains to apply data-driven methods to help solve domain-specific problems and achieve the "Smart Manufacturing" and "Industry 4.0" goals of integrating computing machine intelligence into their respective manufacture production processes [1][2][3][4][5][6][7][10][11][12][13][14][15]. Numerous surveys about the applications of these AI/ML methods to different manufacturing industries have been published.…”

“…The availability of such unprecedented amounts of data together with the recent advances of artificial intelligence (AI) technologies such as ensemble learning, artificial neural networks (ANNs), etc., stimulate the incorporation of machine learning (ML)-based approaches into industrial manufacturing. For example, many such efforts in the cement industry are already underway to test and refine machine learning approaches to improve the control of their production devices including raw mills [1,2], rotary kiln [3,4], ball mills [5][6][7], conveyors [8,9], blenders [10], as well as other related manufacturing activities such as cement clinker quality control [11], concrete porosity prediction [12], energy consumption estimation [13], electricity cost optimization [14], hydrating behavior prediction [15], fault detection and diagnosis [4], etc.…”