Smart solutions for clean air: An AI-guided approach to sustainable industrial pollution control in coal-fired power plant.

Conventional fossil fuels are relied on heavily to meet the ever-increasing demand for energy required by human activities. However, their usage generates significant air pollutant emissions, such as NOx, SOx, and particulate matter. As a result, a complete air pollutant control system is necessary. However, the intensive operation of such systems is expected to cause deterioration and reduce their efficiency. Therefore, this study evaluates the current air pollutant control configuration of a coal-powered plant and proposes an upgraded system. Using a year-long dataset of air pollutants collected at 30-min intervals from the plant's telemonitoring system, untreated flue gas was reconstructed with a variational autoencoder. Subsequently, a superstructure model with various technology options for treating NOx, SOx, and particulate matter was developed. The most sustainable configuration, which included reburning, desulfurization with seawater, and dry electrostatic precipitator, was identified using an artificial intelligence (AI) model to meet economic, environmental, and reliability targets. Finally, the proposed system was evaluated using a Monte Carlo simulation to assess various scenarios with tightened discharge limits. The untreated flue gas was then evaluated using the most sustainable air pollutant control configuration, which demonstrated a total annual cost, environmental quality index, and reliability indices of 44.1 × 106 USD/year, 0.67, and 0.87, respectively.

Future Paradigm of 3D Printed Ni-Based Metal Organic Framework Catalysts for Dry Methane Reforming: Techno-economic and Environmental Analyses.

Dry reforming of biogas is referred as an attractive path for sustainable H2 production over decades. Meanwhile, in the Malaysian context, the abundance of palm oil mill effluent (POME) produced annually is deemed as a potential renewable source for renewable energy generation. Conventionally, nickel (Ni) is the most common catalyst used in the industrial-scale dry reforming of methane (DRM) to yield H2, but it is subject to the drawbacks of sintering and deactivation after a long reaction time at high temperatures (>500 °C). Therefore, this work aims to provide an insight on the feasibility of the application of modified Ni-based catalysts in DRM, specifically in the economic and environmental aspects. From the benchmarking study of various Ni-based catalysts (e.g., bimetallic (Ni-Ce/Al2O3), alumina support (Ni/Al2O3), protonated titanate nanotube (Ni-HTNT), and unsupported), the Ni-MOF catalyst, notably, had proven its prominence in both economic and environmental aspects on the same basis of 10 tonnes of H2 production. The MOF-based catalyst not only possessed a better economic performance (net present value 61.86%, 140%, and 563.08% higher than that of Ni-Ce/Al2O3, Ni/Al2O3, and Ni-HTNT) but also had relatively lower carbon emissions (13.18%, 20.09%, and 75.72% lower than that of Ni/Al2O3, Ni-HTNT, and unsupported Ni). This work also accounted for 3D printing technology for the mass production of Ni-MOF catalysts, where the net present value was 2 to 3% higher than that of the conventional production method. Additionally, sensitivity analysis showed that the H2 price has the greatest impact on the feasibility of DRM as compared to other cost factors.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products.

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review.

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry.

Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization.

The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion.