Life cycle assessment and net present worth analysis of a community-based food waste treatment system

Food waste management has been a global challenge with significant economic and environmental impacts. A community-based food waste treatment scheme for Glasgow, UK is proposed. The food waste was treated by small-scale wet, mesophilic anaerobic digestion. Biogas was combusted in a combined heat and power plant to generate heat and electricity for each community. 201.39 kWh of electricity and 246.09 kWh of thermal energy could be provided to local communities per tonne of food waste treated. A total of 52,762 tonnes of food waste were produced each year in the city. Net-present worth analysis was employed to evaluate the scheme's economic feasibility. The scheme's environmental impacts were evaluated using life cycle assessment. The entire system saved 92.27 kg CO2-eq. per tonne of food waste treated and had a net-present worth of £ 3.187 million with a carbon tax of 50 £ tonne−1 and a biogas yield of 190 m3 tonne−1

Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

Gasification technologies have been extensively studied for their potential to convert biomass feedstocks into syngas (a mixture of CH4, H2, and CO mainly) that can be further turned into heat or electricity upon combustion. It is crucial to understand optimal gasification process parameters for practical design and operation for maximizing the potential. This study combined the Monte Carlo simulation approach, gasification kinetic modeling, and the random forest algorithm to predict the optimal gasification process parameters (i.e. water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature) towards a maximum syngas yield. The Monte Carlo approach randomly generated a data pool of the process parameters following either a normal or uniform distribution, which was then fed into a validated kinetic model to create 2,000 datasets (process parameters and syngas yields). For the random forest model, the mean decrease accuracy and mean decrease Gini were used to assess the importance of the process parameters on syngas yields. The accuracy of the optimization method was evaluated using the coefficient of determination (R2), the root means square error (RMSE), and the mean absolute error (MAE). Generally, the predictions for the normal distribution case were closer to the experimental data obtained from existing literature than that for the uniform distribution case. The model was used to predict the optimal syngas yield and process parameters of wood gasification and it was shown that the predictions were generally in good agreement (<12% difference for the case of normal distribution) with existing experimental results. The method serves as a useful tool for determining optimal gasification process parameters for process and operation design

Green strategies for sustainable packaging

No abstract available

Preventing the immense increase in the life-cycle energy and carbon footprints of LLM-powered intelligent chatbots

Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several alternatives beyond the famous ChatGPT. However, training, fine-tuning, and updating such intelligent chatbots consume substantial amounts of electricity, resulting in significant carbon emissions. The research and development of all intelligent LLMs and software, hardware manufacturing (e.g., graphics processing units and supercomputers), related data/operations management, and material recycling supporting chatbot services are associated with carbon emissions to varying extents. Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact. In this work, we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots. Based on a life-cycle and interaction analysis of these phases, we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints. While anticipating the enormous potential of this advanced technology and its products, we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development

Machine learning toward improving the performance of membrane-based wastewater treatment: A review

Machine learning (ML) is a data-driven approach that can be applied to design, analyze, predict, and optimize a process based on existing data. Recently, ML has found its application in improving membrane separation performance for wastewater treatment. Models have been developed to predict the performance of membranes to separate contaminants from wastewater, design optimum conditions for membrane fabrication for greater membrane separation performance and predict backwashing membranes and membrane fouling. This review summarizes the progress of ML-based membrane separation modeling and explores the direction of the future development of ML in membrane separation-based wastewater treatment. The strengths and drawbacks of the ML algorithms extensively used in membrane separation-based wastewater treatment are summarized. Artificial neural network (ANN) was the most used algorithm for modeling membrane separation-based wastewater treatment. Future research is recommended to focus on the development of integrated ML algorithms and on combining ML algorithms with other modeling approaches (e.g., process-based models and statistical models). This will serve to achieve higher accuracy and better performance of the ML application

A review of high-solid anaerobic digestion (HSAD):From transport phenomena to process design

High-solid anaerobic digestion (HSAD) is an attractive organic waste disposal method for bioenergy recovery and climate change mitigation. The development of HSAD is facing several challenges such as low biogas and methane yields, low reaction rates, and ease of process inhibition due to low mass diffusion and mixing limitations of the process. Therefore, the recent progress in HSAD is critically reviewed with a focus on transport phenomena and process modelling. Specifically, the work discusses hydrodynamic phenomena, biokinetic mechanisms, HSAD-specific reactor simulations, state-of-the-art multi-stage reactor designs, industrial ramifications, and key parameters that enable sustained operation of HSAD processes. Further research on novel materials such as bio-additives, adsorbents, and surfactants can augment HSAD process efficiency, while ensuring the stability. Additionally, a generic simulation tool is of urgent need to enable a better coupling between biokinetic phenomena, hydrodynamics, and heat and mass transfer that would warrant HSAD process scale-up

Smart membranes for oil/water emulsions separation: a review

Oily wastewater poses a significant impact on both environments and human societies. Especially, the treatment of oil/water emulsions for separating oil from water is challenging due to the high stability of oil/water emulsions. Smart membranes, known as stimuli-responsive membranes, are one of the emerging technologies that have been paid wide attention for separating oil/water emulsions in recent years. Smart membranes possess the unique features of switchable wettability between hydrophilicity and hydrophobicity after being triggered by external stimuli and have desired anti-fouling properties. This review summarizes the development of smart membranes for oil/water emulsions separation during the past five years (2018 – present). It was found that solvent stimuli-responsive membranes are the most popular type of smart membranes for oil/water emulsions separation. For multi-stimuli-responsive membranes that can respond to more than one stimulus, future research should focus on developing appropriate fabrication strategies to increase the separation and anti-fouling performances of the membranes. Additionally, surface coating, surface grafting, and copolymer blending are the most popular methods for smart membranes fabrication. However, these methods might not be universally applicable to the different types of stimuli-responsive membranes

Enhancement of bunker oil biodesulfurization by adding surfactant

Biodesulfurization (BDS) is a promising method to remove sulfur compounds from diesel and gasoline. However, the information on BDS of heavy oil is scanty, which might be due to their "undesirable" physical properties and more complicated sulfur diversities. In this study, the BDS of one kind of heavy oil, bunker oil MFO380 was investigated. The biocatalyst was obtained by the enrichment with oil sludge as the seed and using dibenzothiophene (DBT) as the sole sulfur source. The enriched biocatalyst (microbial mixed culture) could selectively remove sulfur from DBT and DBT was transformed into 2-hydroxybiphenyl, which indicates that the BDS process is beneficial to non-destructive carbon bonds and thus can maintain the calorific value. The bunker oil BDS results showed that after 7 days of incubation, the removal efficiency of sulfur in MFO380 was only 2.88 %, but this could be significantly improved by adding surfactants Triton X-100 or Tween 20. This effect could be attributed to greatly reduced viscosity of heavy oil and increased mass transfer of sulfur compounds in heavy oil into water. Adding Triton X-100 achieved the highest removal efficiency of sulfur, up to 51.7 % after 7 days of incubation. The optimal amount of Triton X-100 was 0.5 g/50 ml medium. When toluene was added as an organic solvent for MFO380, the BDS activity was improved, while lower than the effect of adding surfactants

Enhancement of anaerobic digestion of grass by pretreatment with imidazolium-based ionic liquids

10.1080/09593330.2016.1238963Environmental Technology38151843-185

Enhancement of Biogas Yield of Poplar Leaf by High-Solid Codigestion with Swine Manure

The aim of this work was to examine the improvement of anaerobic biodegradability of organic fractions of poplar leaf from codigestion with swine manure (SM), thus biogas yield and energy recovery. When poplar leaf was used as a sole substrate, the cumulative biogas yield was low, about 163 mL (g volatile solid (VS))(-1) after 45 days of digestion with a substrate/inoculum ratio of 2.5 and a total solid (TS) of 22 %. Under the same condition, the cumulative biogas yield of poplar leaf reached 321 mL (g VS)(-1) when SM/poplar leaf ratio was 2:5 (based on VS). The SM/poplar leaf ratio can determine C/N ratio of the cosubstrate and thus has significant influence on biogas yield. When the SM/poplar leaf ratio was 2:5, C/N ratio was calculated to be 27.02, and the biogas yield in 45 days of digestion was the highest. The semi-continuous digestion of poplar leaf was carried out with the organic loading rate of 1.25 and 1.88 g VS day(-1). The average daily biogas yield was 230.2 mL (g VS)(-1) and 208.4 mL (g VS)(-1). The composition analysis revealed that cellulose and hemicellulose contributed to the biogas production