The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R² score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Cement, as a construction material, has low thermal resistance, inherent fire resistance, and is incombustible up to a certain degree. However, the loss of its mechanical performance and spalling are its primary issues, and it thus cannot retain its performance in refractory applications. The present study explores the performance of geopolymer formulations that have excellent fire resistance properties for potential refractory applications. This study is unique, as it investigates advanced solid geopolymer formulations that need only water to activate and bind. Various solid geopolymer formulations with fly ash as a precursor; potassium hydroxide and potassium silicate as activators; and mullite and alumina as refractory aggregates were studied for their compressive strength at up to 1100 °C and compared with their two-part conventional liquid alkaline geopolymer counterparts. Advanced solid geopolymer formulations with mullite and alumina as refractory aggregates had mechanical strength values of 84 MPa and 64 MPa post-1100 °C exposure and were further exposed to ten thermal cycles of 1100 °C to study their fatigue resistance and post-exposure compressive strengths. The geopolymer sample with mullite as a refractory aggregate yielded 115.2 MPa compressive strength after the fourth cycle of exposure. This sample was also studied for its temperature distribution upon direct flame exposure. All the geopolymer formulations displayed a drop in compressive strength at 600 °C due to viscous sintering and then a rise in strength at 1100 °C due to phase transformation. X-ray diffraction studies revealed that the formation of crystalline phases such as leucite, sanidine, and annite were responsible for the superior strengths at 1100 °C for the alumina- and mullite-based geopolymer formulations.

An Experimental Investigation of Accelerated Carbonation on Properties of Concrete