MACHINE LEARNING ALGORITHM TO PREDICT CO2 USING A CEMENT MANUFACTURING HISTORIC PRODUCTION VARIABLES DATASET: A CASE STUDY AT UNION BRIDGE PLANT, HEIDELBERG MATERIALS, MARYLAND

This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO2 during cement manufacturing.Calcination is necessary to determine the clinker quality, energy needs, and CO2 emissions in a cement-producing facility.Due to the intricacy of the calci

ObjectivesThis study examines the impact of Europe/USA/Japan extreme temperatures on hospital utilization and public health insurance program spending in a country with no universal health coverage.MethodsUsing nationwide U.S.county-level panel data and a fixed effects model, we estimate the impact of annual variations in the number of hot and cold

We applied common (pH, elemental analysis, thermogravimetry) and less-common (infrared spectroscopy, GACS adsorption test, pyrolysis-GC-MS, hydrogen pyrolysis) analytical procedures to a set of biochars from Costa Rica (bamboo stalk, Spirulina cacao chaff, sawmill scrap, coconut husk and orchard prunings feedstocks).The biochars were produced by hi

Objective: During the ancient times, saffron (Crocus sativus L.) had many uses around the world; however, some Spirulina of these uses were forgotten throughout the history.But a newly formed interest in natural active compounds brought back the attention toward historical uses of saffron.Understanding different uses of saffron in the past can help

Artificial Neural Network (ANN) learns from inputs and outputs.The values of the weights and biases in ANN are updated according to inputs and outputs.Researchers have proposed algorithms to train Multi-Layer Perceptron (MLP).However, classical techniques often face problems in solving this optimization problem.They tend to need large amounts of co