Biochar production and its environmental applications: Recent developments and machine learning insights.

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

Review on machine learning-based bioprocess optimization, monitoring, and control systems.

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Sericin-coated polyester based air-filter for removal of particulate matter and volatile organic compounds (BTEX) from indoor air.

Particulate matter and volatile organic compounds have emerged as a prime environmental concern with increasing air pollution in metropolitan cities leading to lung and heart-related issues. This paper describes a facile and novel method for fabrication of polyester based air filter via surface coating with Sericin for imparting effective removal of particulate matter and volatile organic compounds. A simple dip-coating method followed by thermal fixation has been adopted to coat Sericin on the polyester fiber. The developed changes in surface functionality and morphology of the polyester fiber were confirmed by Attenuated total reflection Fourier-transform infrared spectroscopy and Field emission scanning electron microscopy analysis. The fabricated air filter was tested for removal of particulate matter (generated burning incense stick) and volatile organic compounds (generated vaporizing gasoline), in an indoor chamber. The Sericin coated filter was able to remove the PM2.5 and PM 10 (from 1000 µg/m3 level to 5 µg/m3 in a 6.28 m3 chamber) within 27 and 23 min of operation, respectively. The fabricated filter very effectively removed particulate matter for 2160 cycles with intermittent washing. The Sericin-coated air filter also proved very effective for removal of volatile organic compounds (Benzene, Toluene, Ethylbenzene and Xylene) from an indoor chamber at a varying initial concentration of 100-1000 µg/m3. The adsorption behavior was described by Langmuir-Freundlich (sips) isotherm and pseudo-first order kinetics with minimal error. The maximum adsorption capacity (mg/g) obtained with Sips Isotherm fitting followed the order Xylene (6.97)>Ethyl Benzene (5.68)> Toluene (5.35) >Benzene (4.78).