Intelligent approaches for sustainable management and valorisation of food waste

Valorization of food waste: A comprehensive review of individual technologies for producing bio-based products

BACKGROUND

The escalating global concerns about food waste and the imperative need for sustainable practices have fuelled a burgeoning interest in the valorization of food waste. This comprehensive review delves into various technologies employed for converting food waste into valuable bio-based products. The article surveys individual technologies, ranging from traditional to cutting-edge methods, highlighting their respective mechanisms, advantages, and challenges.

SCOPE AND APPROACH

The exploration encompasses enzymatic processes, microbial fermentation, anaerobic digestion, and emerging technologies such as pyrolysis and hydrothermal processing. Each technology's efficacy in transforming food waste into bio-based products such as biofuels, enzymes, organic acids, prebiotics, and biopolymers is critically assessed. The review also considers the environmental and economic implications of these technologies, shedding light on their sustainability and scalability. The article discusses the role of technological integration and synergies in creating holistic approaches for maximizing the valorization potential of food waste. Key finding and conclusion: This review consolidates current knowledge on the valorization of food waste, offering a comprehensive understanding of individual technologies and their contributions to the sustainable production of bio-based products. The synthesis of information presented here aims to guide researchers, policymakers, and industry stakeholders in making informed decisions to address the global challenge of food waste while fostering a circular and eco-friendly economy.

The Impact of Food Waste Compost, Vermicompost, and Chemical Fertilizers on the Growth Measurement of Red Radish (Raphanus sativus): A Sustainability Perspective in the United Arab Emirates

The pressing need for sustainable agricultural practices, especially with the increasing population, has directed attention towards alternative fertilizers that enhance crop yield while preserving soil integrity and reducing food loss. The current study investigated the comparative efficacy of food waste compost (FOWC), vermicompost, and chemical fertilizers on the growth of red radish. The present work used a systematic experimental design to evaluate plant growth parameters, including radish weight and height. The soil quality was determined by measuring the pH and electrical conductivity for all soil samples. The results indicated a significant variation in red radish fresh weight among different treatments. For example, the 25% vegetable and fruit waste compost (VFWC) treatment demonstrated a relatively high mean fresh weight, while the 50% mixed compost (MC) treatment yielded a much lower mean fresh weight. These numbers underscore the potential efficacy of specific food waste treatments in enhancing plant growth, with vermicompost at 50% and VFWC at 25% showing considerable promise in increasing crop yield. The current study concluded that FOWC and vermicompost significantly improved plant growth, advocating for their use as sustainable and environmentally friendly alternatives to chemical fertilizers. The current findings emphasized the importance of selecting appropriate fertilizer types and concentrations to optimize agricultural productivity and environmental sustainability, supporting the incorporation of food waste into agricultural systems as a beneficial resource.

Adaptive neuro-fuzzy inference system for forecasting corrosion rates of automotive parts in biodiesel environment

It is precarious to scrutinize the impacts of operational parameters on corrosion when choosing materials for the green diesel and automotive industries. This was the original study to showcase an optimization stratagem for abating corrosion rates (CRs) of automotive parts (APs) explicitly copper and brass in a biodiesel environment, adopting novel Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS).To model CRs, the RSM and ANFIS were utilized. The mechanical properties of APs were inspected, explicitly their hardness number and tensile strength, as well as their outward morphologies. The optimal CRs for copper and brass were 0.01656 mpy and 0.008189 mpy at a B 3.91 biodiesel/diesel blend and 240.9-h exposure. The ANFIS model had a higher coefficient of determination and lower values of root mean squared errors (RMSE), mean average error (MAE), and average absolute deviation (AAD) when compared to the RSM model; this authenticates the ANFIS model's superiority for predicting CRs of copper and brass. The tensile strength of brass was greater than that of copper, while the latter had a higher hardness number. The information, model, and correlations can assist APS in mitigating and slaving over for the corrosiveness of APs while utilizing green diesel.

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.

The analysis of classical, polynomial regression and cubic spline mathematical models in hemp biodiesel optimization: an experimental comparison

Post-pandemic inflationist pressures, climate changes and extremes, regional conflicts, and soaring food prices caused the food crisis to increase rapidly worldwide. This global problem directs producers and researchers to use oils used as feedstock in biodiesel production effectively. In this context, it is important to assay the transesterification parameters and conduct new optimization studies to increase biodiesel yield. In this study, methyl ester was produced from hemp oil by transesterification using sodium hydroxide (NaOH). Next, classical optimization study was carried out to determine the effects of catalyst amount, alcohol:oil molar ratio, reaction temperature, and reaction time variables on biodiesel yield. Secondly, the cubic spline mathematical model (CSMM) and polynomial regression mathematical model (PRMM) were applied to the first data of this optimization. Among these optimization methods, the utmost biodiesel yield registered was 96.115% at hemp seed oil (HSO):methanol molar ratio of 5.59:1, catalyst concentration of 0.531 wt%, reaction temperature of 42.5 °C, reaction time of 62.1 min, and agitation intensity of 600 rpm at PRMM. Some vital fuel properties obtained from HSO biodiesels as a result of three optimizations satisfied the EN 14214 standard. The results illustrated that the optimal yields from CSMM and PRMM are 0.765% and 1.065% higher, respectively, according to the maximum efficiency obtained from the classical optimization. The outcomes showed that CSMM and PRMM are cost-effective, easy to handle, and promising new approaches.

Machine learning-based prediction of methane production from lignocellulosic wastes

The biochemical methane potential test is a standard method to determine the biodegradability of lignocellulosic wastes (LWs) during anaerobic digestion (AD) with disadvantages of long experiment duration and high operating expense. This paper developed a machine learning model to predict the cumulative methane yield (CMY) using the data of 157 LWs regarding physicochemical characteristics, digestion condition and methane yield, with the coefficient of determination equal to 0.869. Model interpretability analyses underscored lignin content, organic loading, and nitrogen content as pivotal attributes for CMY prediction. For the feedstocks with a cellulose content exceeding about 50%, the CMY in the early AD stage would be relatively lower than those with low cellulose content, but prolonging digestion time could promote methane production. Besides, lignin content in feedstock surpassing 15% would significantly inhibit methane production. This work contributes to valuable guidance for feedstock selection and operation optimization for AD plants.

Dietary Bioactive Compounds: Implications for Oxidative Stress and Inflammation

Nowadays, it has been amply demonstrated how an appropriate diet and lifestyle are essential for preserving wellbeing and preventing illnesses [...].

Enhancing methane production from food waste with iron-carbon micro-electrolysis in a two-stage process

A two-stage process, consisting of a leach-bed reactor (LBR) and an up-flow anaerobic sludge blanket reactor (UASB), has been commonly adopted to improve food waste anaerobic digestion. However, its application is limited due to low hydrolysis and methanogenesis efficiencies. This study proposed a strategy of incorporating iron-carbon micro-electrolysis (ICME) into the UASB and recirculating its effluent to the LBR to improve the two-stage process efficiency. Results showed that the integration of the ICME with the UASB significantly increased the CH4 yield by 168.29%. The improvement of the food waste hydrolysis in the LBR mainly contributed to the enhanced CH4 yield (approximately 94.5%). The enrichment of hydrolytic-acidogenic bacterial activity, facilitated by the Fe2+ generated through ICME, might be the primary cause of the improved food waste hydrolysis. Moreover, ICME enriched the growth of hydrogenotrophic methanogens and stimulated the hydrogenotrophic methanogenesis pathway in the UASB, contributing partially to the enhanced CH4 yield.

Achieving a win-win situation by promoting internet recycling of waste electronics and preventing information leakage in a multi-party game

With scientific and technological advancements and the diversification of residents' demands, the pace of electronic product replacement is accelerating, generating a rapidly increasing number of waste electronics. The rapid development of information technologies, such as the Internet, has brought significant opportunities for recycling waste electronics. However, this is hindered by information safety concerns from residents. To achieve a win-win situation of promoting Internet recycling of waste electronics and preventing information leakage, this study performed a game analysis using key stakeholders in the Internet recycling of waste electronics. The game analysis of recycling waste electronics revealed that the lower the personal information leakage, the more residents would participate in recycling. Strict government regulation would increase the credibility of Internet recycling companies in protecting information security. Further, if the government imposed high fines on companies that breach information security, Internet recycling companies would endeavor to protect information security. In conclusion, this study offers policy recommendations and a theoretical basis to achieve a win-win situation of promoting Internet recycling of waste electronics and preventing information leakage from the perspective of stakeholders.