Machine learning for high solid anaerobic digestion: Performance prediction and optimization.

Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively. The statistical analysis showed that there was no significant difference (p = 0.377) across the datasets, wherein with less inputs, accurate results could be predicted. In case of biogas yield, the critical factors which affect the model predictions include loading rate and retention time. The developed high solid machine learning model shows the possibility of integrating Artificial Intelligence to optimize and control AD process, thus contributing to a generic model for enhancing the overall performance of the biogas plant.

Environmental impacts and effects on greenhouse gas emissions in shrimp feed production system for aquaculture - A case study in India.

Attributional life cycle assessment study examines the environmental impact of raw materials, machinery, and unit operations. In the present work, an attributional life cycle assessment (LCA) was employed to assess the environmental and greenhouse gas impacts of a shrimp feed production system. A commercial shrimp feed mill in Tamil Nadu, India, provided inventory data for one-ton shrimp feed (functional unit) for a Cradle-to-Gate evaluation using environmental impact methodologies, specifically Impact 2002+ in SimaPro® (V9.3.0.3) software. The results showed that human health (0.003357 DALY), ecosystem quality (2720.518 PDF × m2 × yr), climate change (2031.696 kg CO2 eq), and resources (71019.42 MJ primary) were the most significantly impacted. The human health category was found to be the most prominent after normalization and weighting (0.47 pt), and strategies were suggested accordingly. The GWP20 and GWP100 measures for long-term climate change were calculated to be 8.7 and 7.33 kg CO2 eq, respectively. Cast iron used in machinery production (GWP 20-15.40%, GWP100-134.5%) and electricity use (GWP 20-6.13%, GWP 100-6.9%) accounted for sizable portions of the burden. Feed production is estimated to contribute 0.2% of global CO2 emissions within the proposed global context. These findings are significant regarding economically and environmentally sustainable shrimp feed production worldwide.

Advanced approaches for resource recovery from wastewater and activated sludge: A review.

Due to resource scarcity, current industrial systems are switching from waste treatment, such as wastewater treatment and biomass, to resource recovery (RR). Biofuels, manure, pesticides, organic acids, and other bioproducts with a great market value can be produced from wastewater and activated sludge (AS). This will not only help in the transition from a linear economy to a circular economy, but also contribute to sustainable development. However, the cost of recovering resources from wastewater and AS to produce value-added products is quite high as compared to conventional treatment methods. In addition, most antioxidant technologies remain at the laboratory scale that have not yet reached the level at industrial scale. In order to promote the innovation of resource recovery technology, the various methods of treating wastewater and AS to produce biofuels, nutrients and energy are reviewed, including biochemistry, thermochemistry and chemical stabilization. The limitations of wastewater and AS treatment methods are prospected from biochemical characteristics, economic and environmental factors. The biofuels derived from third generation feedstocks, such as wastewater are more sustainable. Microalgal biomass are being used to produce biodiesel, bioethanol, biohydrogen, biogas, biooils, bioplastics, biofertilizers, biochar and biopesticides. New technologies and policies can promote a circular economy based on biological materials.

Economic perspectives and policy insights on carbon capture, storage, and utilization for sustainable development.

Carbon capture storage and utilization (CCSU) has the potential to become a key tool to mitigate climate change, thus, aiding in achieving the objectives of the 2015 Paris Agreement. Even though the relevant remediation technology has achieved technical maturity to a certain extent, implementation of CCSU on a larger scale is currently limited because of non-technical parameters that include cost, legalization, lack of storage reservoir, and market mechanism to penalize CO2 emitter. Among these, cost emerges as the primary barrier to the dissemination of CCSU. Hence, necessary policy frameworks and incentives must be provided by governing agencies to enable faster dissemination of carbon capture and utilization (CCU) and carbon capture and storage (CCS) globally. Meanwhile, strict implementation of a carbon tax across nations and market demand for products generated using captured CO2 can aid in the fast adoption of CCU and CCS. This review assessed the economic feasibility and sustainability of CCS and CCU technologies to identify the barriers to commercializing these technologies.

Dynamic simulation and optimization of anaerobic digestion processes using MATLAB.

Time series-based modeling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill's model using MATLAB, which can predict biomethane production with time series. This model can predict the biomethane production for both batch and continuous process, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between literature and the developed model was less than ± 7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary to an industrial perspective, which usually requires extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.