Trigeneration based on the pyrolysis of rural waste in India: Environmental impact, economic feasibility and business model innovation.

Pyrolysis-based waste-to-bioenergy development has the potential to resolve some of the major challenges facing rural communities in India such as poor electrification, household air pollution, and farmland degradation and contamination. Existing understanding and analysis of the economic feasibility and environmental impact of bioenergy deployment in rural areas is limited by parameter uncertainties, and relevant business model innovation following economic evaluation is even scarcer. This paper uses findings from a new field survey of 1200 rural households to estimate the economic feasibility and environmental impact of a pyrolysis-based bioenergy trigeneration development that was designed to tackle these challenges. Based on the survey results, probability distributions were constructed and used to supply input parameters for cost-benefit analysis and life cycle assessment. Monte Carlo simulation was applied to characterise the uncertainties of economic feasibility and environmental impact accounting. It was shown that the global warming potential of the development was 350 kg of CO2-eq per capita per annum. Also, the survey identified a significant mismatch between feedstock prices considered in the literature and prices asked for by the surveyed villagers. The results of the cost-benefit analysis and life cycle assessment were then applied to propose two novel business models inspired by the Business Model Canvas, which had the potential to achieve up to 90 % economic profitability and result in a benefit-cost ratio of 1.35-1.75. This is the first study achieving combined environmental and economic analysis and business model innovation for rural bioenergy production in developing countries.

Reductive soil disinfestation and Fe amendment improve soil microbial composition and Fritillaria production.

The continuous obstacles of cropping cause severe economic loss, which seriously threaten agricultural sustainable development. In addition, managing excess waste, such as potato peel and mineral waste residues, is a vital burden for industry and agriculture. Therefore, we explored the feasibility of reductive soil disinfestation (RSD) with potato peel and amendment with iron mineral waste residues for the production of Fritillaria thunbergii, which is vulnerable to continuous obstacles. In this study, the influences of iron mineral, RSD with different organic maters, as well as the combined effects of iron mineral and RSD on Fritillaria rhizosphere soil physicochemical properties, microbial communities, and Fritillaria production were investigated. The results revealed that the RSD treatments with potato peel significantly reduced the soil salinity and increased the soil pH, microbial activity, organic matter, and the contents of K and Ca. RSD with potato peel also significantly thrived of the beneficial microbes (Bacillus, Azotobacter, Microvirga, and Chaetomium), and down-regulated potential plant pathogens. RSD with potato peel significantly promoted F. thunbergii yield and quality. Moreover, the combined effects of RSD and iron mineral amendment further enhanced soil health, improved microbial community composition, and increased the yield and peimisine content of F. thunbergii by 24.2% and 49.3%, respectively. Overall, our results demonstrated that RSD with potato peel and amendment with iron mineral waste residues can efficiently improve soil fertility, modify the microbial community, and benefit for both the sustainable production of F. thunbergii and the management of waste. KEY POINTS: • RSD increases soil pH, organic matter, microbial activity, and mineral content • RSD with potato peel enriches beneficial microbes and decreases plant pathogens • PP + Fe treatment increases Fritillaria yield by 24.2% and peimisine content by 49.3.

Metagenomic insights into the inhibitory mechanisms of Cu on fermentative hydrogen production.

Cu is widely present in the feedstocks of dark fermentation, which can inhibit H2 production efficiency of the process. However, current understanding on the inhibitory mechanisms of Cu, especially the microbiological mechanism, is still lacking. This study investigated the inhibitory mechanisms of Cu2+ on fermentative hydrogen production by metagenomics sequencing. Results showed that the exposure to Cu2+ reduced the abundances of high-yielding hydrogen-producing genera (e.g. Clostridium sensu stricto), and remarkably down-regulated the genes involved in substrate membrane transport (e.g., gtsA, gtsB and gtsC), glycolysis (e.g. PK, ppgK and pgi-pmi), and hydrogen formation (e.g. pflA, fdoG, por and E1.12.7.2), leading to significant inhibition on the process performances. The H2 yield was reduced from 1.49 mol H2/mol-glucose to 0.59 and 0.05 mol H2/mol-glucose upon exposure to 500 and 1000 mg/L of Cu2+, respectively. High concentrations of Cu2+ also reduced the rate of H2 production and prolonged the H2-producing lag phase.

Artificial intelligence and machine learning for smart bioprocesses.

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.

Role of silica-based porous cellulose nanocrystals in improving water absorption and mechanical properties.

Epoxy resins are important thermosetting polymers. They are widely used in many applications i.e., adhesives, plastics, coatings and sealers. Epoxy molding compounds have attained dominance among common materials due to their excellent mechanical properties. The sol-gel simple method was applied to distinguish the impact on the colloidal time. The properties were obtained with silica-based fillers to enable their mechanical and thermal improvement. The work which we have done here on epoxy-based nanocomposites was successfully modified. The purpose of this research was to look into the effects of cellulose nanocrystals (CNCs) on various properties and applications. CNCs have recently attracted a lot of interest in a variety of industries due to their high aspect ratio, and low density which makes them perfect candidates. Adding different amounts of silica-based nanocomposites to the epoxy system. Analyzed with different techniques such as Fourier-transformed infrared spectroscope (FTIR), thermogravimetric analysis (TGA) and scanning electronic microscopic (SEM) to investigate the morphological properties of modified composites. The various %-age of silica composite was prepared in the epoxy system. The 20% of silica was shown greater enhancement and improvement. They show a better result than D-400 epoxy. Increasing the silica, the transparency of the films decreased, because clustering appears. This shows that the broad use of CNCs in environmental engineering applications is possible, particularly for surface modification, which was evaluated for qualities such as absorption and chemical resistant behavior.

Review of explainable machine learning for anaerobic digestion.

Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.

Review of biochar production via crop residue pyrolysis: Development and perspectives.

Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.

Integration of anaerobic digestion with heat Pump: Machine learning-based technical and environmental assessment.

Anaerobic digestion (AD)-based biogas production mitigates the environmental footprint of organic wastes (e.g., food waste and sewage sludge) and facilitates a circular economy. The work proposed an integrated system where the thermal energy demand of an AD is supplied using an air source heat pump (ASHP). The proposed system is compared to a baseline system, where the thermal energy is supplied by a natural gas-based heating system. Several machine learning models are developed for predicting biogas production, among which the Gaussian Process Regression (GPR) showed a superior performance (R2 = 0.84 and RMSE = 0.0755 L gVS-1 day-1). The GPR model further informed a thermodynamic model of the ASHP, which revealed the maximum biogas yield to be approximately 0.585 L.gVS-1.day-1 at an optimal temperature of 55 °C (thermophilic). Subsequently, life cycle assessment showed that ASHP-based AD heating systems achieved 28.1 % (thermophilic) and 36.8 % (mesophilic) carbon abatement than the baseline system.

Interpretable machine learning to model biomass and waste gasification.

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R2) of 0.90 when averaged across ten key gasification outputs. Global and local model interpretability methods have been used to illuminate the developed black box models. The studied models were most strongly influenced by the feedstock's particle size and the type of gasifying agent employed. By combining global and local interpretability methods, the understanding of black box models has been improved. This allows policy makers and investors to make more educated decisions about gasification process design.

Potato peel waste for fermentative biohydrogen production using different pretreated culture.

How to manage potato peel waste sustainably has been an issue faced by the potato industry. This work explored the feasibility of potato peel waste for biohydrogen production via dark fermentation, and investigated the effects of various inoculum enrichment methods (acid, aeration, heat-shock and base) on the process efficiency. It was observed that the hydrogen production showed a great variation when using various inoculum enrichment methods, and the aeration enriched inoculum obtained the maximum hydrogen yield of 71.0 mL/g-VSadded and VS removal of 28.9 %. Different enriched cultures also exhibited huge variations in the bacterial community structure and metabolic pathway. The highest abundance of Clostridium sensu stricto fundamentally contributed to the highest process efficiency for the fermenter inoculated with aeration treated culture. This work puts forward a promising strategy for recycling potato peel waste, and fills a gap in the optimal inoculum preparation method for biohydrogen fermentation of potato peel waste.

Process water recirculation for catalytic hydrothermal carbonization of anaerobic digestate: Water-Energy-Nutrient Nexus.

The process water (PW) from acid-catalyzed hydrothermal carbonization (HTC) is still an environmental burden due to the enriched organics, nutrients, and salts. This study proposed a novel strategy to valorize food waste digestate into multifunctional hydrochar by recirculating the PW in the HCl-catalyzed HTC process. The produced multifunctional hydrochar could be utilized as a high-quality solid fuel with HHV of 27.9 MJ kg-1 (hydrochar without PW recirculation) and a slow-release fertilizer by converting the complex Ca and P compounds from the food waste digestate into a Ca-P deposit (hydroxyapatite) with more than a 93 % P recovery rate (hydrochar with PW recirculation). Adding fresh HCl in the HTC PW recirculation system only displayed a marginal catalytic impact on the hydrochar properties after two cycles of recirculation. This study demonstrated the importance of inherent Ca in the feedstocks and the dual role of HCl in the HTC with PW recirculation.

Towards understanding respiratory particle transport and deposition in the human respiratory system: Effects of physiological conditions and particle properties.

Fly ash is a common solid residue of incineration plants and poses a great environmental concern because of its toxicity upon inhalation exposure. The inhalation health impacts of fly ash is closely related to its transport and deposition in the human respiratory system which warrants significant research for health guideline setting and inhalation exposure protection. In this study, a series of fly ash transport and deposition experiments have been carried out in a bifurcation airway model by optical aerosol sampling analysis. Three types of fly ash samples of different morphologies were tested and their respiratory deposition and transport processes were compared. The deposition efficiencies were calculated and relevant transport dynamics mechanisms were discussed. The influences of physiological conditions such as breathing rate, duration, and fly ash physical properties (size, morphology, and specific surface area) were investigated. The deposition characteristics of respiratory particles containing SARS-CoV-2 has also been analyzed, which could further provide some guidance on COVID-19 prevention. The results could potentially serve as a basis for setting health guidelines and recommending personal respiratory protective equipment for fly ash handlers and people who are in the high exposure risk environment for COVID-19 transmission.

Life-cycle assessment of pyrolysis processes for sustainable production of biochar from agro-residues.

Net carbon management of agro-residues has been an important pathway for reducing the environmental burdens of agricultural production. Converting agro-residues into biochar through pyrolysis is a prominent management strategy for achieving carbon neutrality in a circular economy, meeting both environmental and social concerns. Based on the latest studies, this study critically analyzes the life cycle assessment (LCA) of biochar production from different agro-residues and compares typical technologies for biochar production. Although a direct comparison of results is not always feasible due to different functional units and system boundaries, the net carbon sequestration potential of biochar technology is remarkably promising. By pyrolyzing agro-residues, biochar can be effectively produced and customized as: (i) alternative energy source, (ii) soil amendment, and (iii) activated carbon substitution. The combination of life cycle assessment and circular economy modelling is encouraged to achieve greener and sustainable biochar production.

Sustainable management of plastic wastes in COVID-19 pandemic: The biochar solution.

To prevent the COVID-19 transmission, personal protective equipment (PPE) and packaging materials have been extensively used but often managed inappropriately, generating huge amount of plastic waste. In this review, we comprehensively discussed the plastic products utilized and the types and amounts of plastic waste generated since the outbreak of COVID-19, and reviewed the potential treatments for these plastic wastes. Upcycling of plastic waste into biochar was addressed from the perspectives of both environmental protection and practical applications, which can be verified as promising materials for environmental protections and energy storages. Moreover, novel upcycling of plastic waste into biochar is beneficial to mitigate the ubiquitous plastic pollution, avoiding harmful impacts on human and ecosystem through direct and indirect micro-/nano-plastic transmission routes, and achieving the sustainable plastic waste management for value-added products, simultaneously. This suggests that the plastic waste could be treated as a valuable resource in an advanced and green manner.

Life cycle assessment of waste-to-hydrogen systems for fuel cell electric buses in Glasgow, Scotland.

Waste-to-hydrogen (WtH) technologies are proposed as a dual-purpose method for simultaneous non-fossil-fuel based hydrogen production and sustainable waste management. This work applied the life cycle assessment approach to evaluate the carbon saving potential of two main WtH technologies (gasification and fermentation) in comparison to the conventional hydrogen production method of steam methane reforming (SMR) powering fuel cell electric buses in Glasgow. It was shown that WtH technologies could reduce CO2-eq emissions per kg H2 by 50-69% as compared to SMR. Gasification treating municipal solid waste and waste wood had global warming potentials of 4.99 and 4.11 kg CO2-eq/kg H2 respectively, which were lower than dark fermentation treating wet waste at 6.6 kg CO2-eq/kg H2 and combined dark and photo fermentation at 6.4 kg CO2-eq/kg H2. The distance emissions of WtH-based fuel cell electric bus scenarios were 0.33-0.44 kg CO2-eq/km as compared to 0.89 kg CO2-eq/km for the SMR-based scenario.

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass.

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.