A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

A sustainable approach to develop antimicrobial composite film incorporating novel Dalbergia reniformis seed-derived microcrystalline cellulose and medicinal Mikania micrantha extract in PVA

This study presents a sustainable approach to develop antimicrobial films (AMFs) using agricultural wastes. Microcrystalline cellulose (MCC) was extracted from a novel source of Dalbergia reniformis seeds (DRS) through chemical hydrolysis, and bioactive powder from Mikania micrantha (MM) leaves was reinforced into a polyvinyl alcohol (PVA) matrix to create antimicrobial films. The morphological, antimicrobial, physical, mechanical, and thermal properties of the films were investigated. MCC (5 %, 10 %, and 15 %) and MM (5 % and 10 %) concentrations were varied to study their effects on film properties. Fourier transform infrared spectroscopy confirmed the elimination of non-cellulosic compounds in MCC and the chemical interactions among film components, while X-ray diffraction analysis revealed improved crystallinity of MCC compared to raw pulp and enhanced crystallinity of AMFs compared to pure PVA. Scanning electron microscopic images demonstrated better adhesion and homogeneous MCC distribution in the PVA matrix up to a concentration 10 % MCC, while higher concentrations caused self-aggregation. The AMFs showed strong antibacterial activity, with inhibition zones of 18.83 mm for S. aureus and 18.55 mm for E. coli at 10 % MM. Anti-inflammatory properties were confirmed, with pure MM reducing swelling by 46.8 % and AMFs with 10 % MM achieving 33.9 % inhibition. Mechanical properties, including tensile strength, increased by 57.7 % with 10 % MCC but declined at 15 % MCC due to aggregation. Conversely, the moisture content, water solubility, and water vapor permeability of the films significantly decreased with up to 10 % MCC. These findings highlight the potential of the developed AMFs for antimicrobial applications in healthcare, food packaging, and other industries.

Harnessing Artificial Intelligence for Sustainable Bioenergy: Revolutionizing Optimization, Waste Reduction, and Environmental Sustainability

Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisation, waste reduction, and environmental sustainability. With its capacity for intelligent decision-making, predictive modelling, and adaptive controls to maximise bioenergy processes, artificial intelligence (AI) emerges as a crucial catalyst for overcoming these obstacles. The focus on particular uses of AI to enhance bioenergy systems. Algorithms for machine learning are essential for forecasting biomass properties, selecting feedstock optimally, and enhancing energy conversion procedures in general. Enhancing real-time adaptability and guaranteeing optimal performance under a range of operational conditions is made possible by the integration of AI-driven monitoring and control systems. Additionally, it looks at how AI supports precision farming methods in bioenergy settings, enhancing crop management strategies and increasing the output of biofuels. AI-guided autonomous systems help with precision planting, harvesting, and processing, which reduces resource use and maximises yield. AI's contribution to advanced biofuel technology by using data analytics and computational models, it can hasten the creation of new, more effective bioenergy sources. AI-driven grid management advancements could guarantee the smooth integration of bioenergy into current energy infrastructures. The revolutionary role that artificial intelligence (AI) has played in bioenergy systems, making a strong case for the incorporation of AI technologies to drive the global energy transition towards a more ecologically conscious and sustainable future.

Microbial Electrochemical Technologies: Sustainable Solutions for Addressing Environmental Challenges

Addressing global challenges of waste management demands innovative approaches to turn biowaste into valuable resources. This chapter explores the potential of microbial electrochemical technologies (METs) as an alternative opportunity for biowaste valorisation and resource recovery due to their potential to address limitations associated with traditional methods. METs leverage microbial-driven oxidation and reduction reactions, enabling the conversion of different feedstocks into energy or value-added products. Their versatility spans across gas, food, water and soil streams, offering multiple solutions at different technological readiness levels to advance several sustainable development goals (SDGs) set out in the 2030 Agenda. By critically examining recent studies, this chapter uncovers challenges, optimisation strategies, and future research directions for real-world MET implementations. The integration of economic perspectives with technological developments provides a comprehensive understanding of the opportunities and demands associated with METs in advancing the circular economy agenda, emphasising their pivotal role in waste minimisation, resource efficiency promotion, and closed-loop system renovation.

Waves of change: Electrochemical innovations for environmental management and resource recovery from water - A review

Environmental electrochemistry and water resource recovery are covered in this review. The study discusses the growing field's scientific basis, methods, and applications, focusing on innovative remediation tactics. Environmental electrochemistry may solve water pollution and extract resources. Electrochemical methods may effectively destroy or convert pollutants. This method targets heavy metals, organic compounds, and emerging water contaminants such as pharmaceuticals and microplastics, making it versatile. Environmental electrochemistry and resource recovery synergize to boost efficiency and sustainability. Innovative electrochemical methods can extract or synthesise metals, nutrients, and energy from wastewater streams, decreasing treatment costs and environmental effect. The study discusses electrocoagulation, electrooxidation, and electrochemical advanced oxidation processes and their mechanics and performance. Additionally, it discusses current electrode materials, reactor designs, and process optimisation tactics to improve efficiency and scalability. Resource recovery in electrochemical remediation methods is also examined for economic and environmental feasibility. Through critical examination of case studies and techno-economic evaluations, it explains the pros and cons of scaling up these integrated techniques. This study covers environmental electrochemistry and resource recovery's fundamental foundations, technology advances, and sustainable water management consequences.

A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment

Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.

Bioenergy relations with agriculture, forestry and other land uses: Highlighting the specific contributions of artificial intelligence and co-citation networks

The concerns with the environment and sustainability have promoted options for energy sources that mitigate the footprint of human life. The use of biomass from agriculture, forestry and other land uses (AFOLU) has enormous potential for the production of bioenergy as a renewable source of energy. In this context, this research aims to analyse the interrelationships between bioenergy and agriculture, forestry and other land uses, highlighting the contributions of the digital transition for these dimensions. To achieve these objectives, a bibliometric analysis through co-citation links (and items related to cited authors, references and sources) was carried out for the dimensions associated with the bioenergy and the AFOLU and after a specific literature survey was performed for the contributions from the digital transition for these frameworks. With this study, top authors, references and sources were identified for the topics assessed and it was highlighted the importance of digital transitions for more efficient bioenergy use and production in the worldwide contexts.

Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects

China's tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

Catalytic torrefaction effect on waste wood boards for sustainable biochar production and environmental remediation

Wood boards used in construction are generally treated with toxic chemicals, making them unsuitable for further use and causing environmental pollution. This study evaluates the possibility of using catalytic torrefaction as a pretreatment to improve wood pyrolysis and combustion for greener biochar production. Waste beech boards were impregnated with different K2CO3 solutions (0-0.012 M), then torrefied between 5 and 60 min at 275 °C. The ICP-AES showed that the board's surface held more potassium than the core. Torrefaction coupled with potassium decreased the C-O and -OH stretches. Thermogravimetric analysis of torrefied wood showed that the board's internal heating degraded the core more than the surface. The exothermic reactions made potassium's catalytic action more efficient in the core. Interactions between the potassium content and torrefaction duration decreased the pyrolysis' maximum devolatilization temperature. During combustion, potassium decreased the ignition temperature by up to 9% and 3% at the surface and core, respectively, while the torrefaction increased it. The catalytic torrefaction significantly decreased the devolatilization peak during combustion, thus making the wood's combustion similar to that of coal, having only the char oxidation step. These findings highlight the advantages and challenges of waste wood's catalytic-torrefaction for biochar production to reduce environmental pollution.