Potential of eggshell waste derived calcium for sustainable production of biogas from cassava wastewater.

Cassava is a staple crop that plays a significant role in the food security of many countries. However, its processing produces a liquid by-product known as cassava wastewater (CW), which can have adverse environmental consequences if discarded without treatment. Despite its cyanide content, CW has a high organic content and may be profitable when used to produce biogas. In this study, the influence of calcium particles from eggshell residues was investigated on the anaerobic digestion of CW. Moreover, the performance of the bioreactor was remotely monitored. Calcium particles from milled-calcined chicken eggshells were added to the bioreactor, and biogas production was investigated for 21 days. Adding 1 g/L and 3 g/L of calcium particles increased biogas (Bio H2 + Bio CH4) production by 195% and 338%, respectively. Finally, the requirement for digestate post-treatment before use in agriculture was observed after assessing its phytotoxicity through the germination and root growth of L. sativa seeds.

Application of machine learning in anaerobic digestion: Perspectives and challenges.

Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.