Around 40% to 60% of municipal solid waste originates from kitchens, offering a valuable resource for compost production. Traditional composting methods such as windrow, vermi-, and bin composting are space-intensive and time-consuming. Black soldier fly larvae (BSFL) present a promising alternative, requiring less space and offering ease of handling. This research encompasses experimental data collection, life cycle assessment, and machine learning, and employs the Levenberg–Marquardt algorithm in an Artificial Neural Network, to optimize kitchen waste treatment using BSFL. Factors such as time, larval population, aeration frequency, waste composition, and container surface area were considered. Results showed that BSFL achieved significant waste reduction, ranging from 70% to 93% by weight and 65% to 85% by volume under optimal conditions. Key findings included a 15-day treatment duration, four times per day aeration frequency, 600 larvae per kilogram of waste, layering during feeding, and kitchen waste as the preferred feed. The larvae exhibited a weight gain of 2.2% to 6.5% during composting. Comparing the quality of BSFL compost to that obtained with conventional methods revealed its superiority in terms of waste reduction (50% to 73% more) and compost quality. Life cycle assessment confirmed the sustainability advantages of BSFL. Machine learning achieved high accuracy of prediction reaching 99.5%.

This groundbreaking review explores the crucial role of the circular economy in industrial chemistry and environmental engineering. It surpasses a mere examination of principles and methods, delving into the profound significance and urgency of this transformative shift. By analyzing key elements such as resource efficiency, waste valorization, sustainable product design, industrial symbiosis, and policy integration, the study highlights the power of collaboration, technological advancements, and extensive literature research. It reveals the remarkable alignment between the circular economy and the Sustainable Development Goals (SDGs), emphasizing how circular practices promote resource efficiency, waste reduction, and sustainable production and consumption patterns, thus driving progress across multiple SDGs. With a specific focus on responsible consumption and production, clean energy, innovative industrial practices, climate action, ecosystem protection, water resource management, job creation, economic growth, sustainable urbanization, and collaboration, the review provides a comprehensive roadmap for adopting circularity. Its practical recommendations cover sustainable material selection, resource efficiency, closing loop, digitalization, and robust policy support. In addition, it emphasizes the paramount importance of collaboration, stakeholder engagement, education, capacity building, circular supply chain management, and effective policy frameworks in spearheading circular economy initiatives. Drawing inspiration from diverse circular economy models and compelling case studies in industrial chemistry, the study highlights the integration of environmental, social, and governance (ESG) factors, ensuring both sustainability and positive societal impact. This comprehensive review serves as a guiding light, demonstrating the immense potential of the circular economy in driving sustainable development. It offers actionable guidance for implementing circular practices, empowering professionals to make tangible contributions to a more sustainable future. Additionally, it serves as a foundational piece, fueling the advancement of knowledge, inspiring further research, and propelling remarkable progress in the ever-evolving fields of industrial chemistry and environmental engineering.

The removal of phenolic compounds is of great importance because of their toxic nature and potentially harmful effects on the environment and human health. This study examines the use of rice husk as a biosorbent for eliminating phenolic compounds, particularly resorcinol, from industrial wastewater. Three types of rice husk, namely raw rice husk (RRH), chemically treated rice husk (CTRH), and thermally treated rice husk (TTRH), are utilized after grinding and methanol treatment. Characterization techniques including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and optical microscopy are used to analyze the rice husk-based adsorbents. The microscopic analysis reveals the presence of nano-pores in TTRH and the existence of carbonyl and hydroxyl groups in all sorbent samples. XRD analysis confirms the presence of silica in biosorbents. This study also examines the influence of dosage and initial concentration on resorcinol sorption. Optimized dosages of 0.5 g (RRH), 0.5 g (CTRH), and 1.5 g (TTRH) result in sorption capacities of 14 mg/g (RRH), 11 mg/g (CTRH), and 5 mg/g (TTRH). Isotherm analysis indicates that the Langmuir isotherm best describes the sorption behavior of TTRH, while the Freundlich isotherm is observed for CTRH, and both RRH and CTRH follow the Temkin isotherm.

The synthesis of many transition metal complexes containing 3,5-diamino-1,2,4-triazole (Hdatrz) as a ligand with different counter anions Br⎺, Cl⎺, ClO4⎺ and SO42- has been studied extensively, but the chemistry of transition metal nitrate and acetate compounds and their reactivity are relatively unexplored. In this research work, two new series of metal(II) complexes (M = Ni, Co, and Zn) {[Ni3(Hdatrz)6(H2O)6](NO3)6 (1), [Co3(Hdatrz)6(H2O)6](NO3)6 (2), [Zn3(Hdatrz)6(H2O)6](NO3)6 (3), [Ni3(Hdatrz)6(H2O)6](OAc)6 (4), [Co3(Hdatrz)6(H2O)6] (OAc)6 (5) and [Zn3(Hdatrz)6(H2O)6](OAc)6 (6)} have been synthesized. These synthesized complexes were characterized by various physicochemical techniques such as UV-vis spectroscopy, Fourier transform infrared spectroscopy, and magnetic susceptibility measurements. All six complexes were found to be trinuclear and bridged through the Hdatrz ligand. Spectral data suggested a distorted octahedral environment around the central metal ions in these complexes. It also revealed that the NH and OH groups are involved in hydrogen bonding. These complexes were tested against the fungal strains Colletotrichum gloeosporioides and Aspergillus niger. These synthesized complexes have not been observed to have antifungal activities. The machine learning K-nearest neighbours evaluates the analytical characteristics and solubility behavior of the metal complexes. Machine learning models provide results with 75% precision.