Enhancing energy conservation and CO₂ emission reduction by optimizing cement clinker production line with aggregate composition

According to major part of the energy-intensive manufacture of cement clinker, the cement sector contributes significantly to global CO₂ emissions. Previous experiments that have already been done on the subject of reducing CO₂ emissions and conserving energy during the production of cement clinker typically overlook the possibility of improving aggregate composition as a means of achieving these goals. While process efficiency is the main focus of current techniques, a thorough investigation of how raw material composition can further boost sustainability is lacking. This work presents a novel strategy for enhancing energy efficiency and lowering CO₂ emissions at the same time through aggregate composition optimization. This double gain has the potential to greatly advance cement industry sustainability initiatives. We examined the data set of cement the clinkers. Recycling cement was thought to use as little as 60%-76% of the energy used to produce clinker and emit less carbon dioxide. We proposed an optimal strategy based on the genetic programming with dynamic fuzzy system ensemble and Convolutional Neural networks (GP-DMFSE-CNN) are used to construct prediction models, which are subsequently refined through the application of one-year operation data, focusing attention to a cement clinker production process. To evaluate the suggested solution works in terms of efficiency, employee's satisfaction ratio, prediction rate, and decision make level. As a result, GHRM on EGB demonstrated by the suggested superior performance over other similar models in terms of energy consumption (350 kwh), carbon emission (150 ton), Cement Quality Index (CQI) (90%), and production rate (140 tons).