Biphasic pretreatment excels over conventional sulfuric acid in pinewood biorefinery: An environmental analysis.

This study assesses the environmental impact of pine chip-based biorefinery processes, focusing on bioethanol, xylonic acid, and lignin production. A cradle-to-gate Life Cycle Assessment (LCA) is employed, comparing a novel biphasic pretreatment method (p-toluenesulfonic acid (TsOH)/pentanol, Sc-1) with conventional sulfuric acid pretreatment (H2SO4, Sc-2). The analysis spans biomass handling, pretreatment, enzymatic hydrolysis, yeast fermentation, and distillation. Sc-1 yielded an environmental impact of 1.45E+01 kPt, predominantly affecting human health (96.55%), followed by ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed 32.61%, 29.28%, and 38.11% to the total environmental burdens, respectively. Sc-2 resulted in an environmental burden of 1.64E+01 kPt, with a primary impact on human health (96.56%) and smaller roles for ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed differently at 22.59%, 12.5%, and 64.91%, respectively. Electricity generation was predominant in both scenarios, accounting for 99.05% of the environmental impact, primarily driven by its extensive usage in biomass handling and pretreatment processes. Sc-1 demonstrated a 13.05% lower environmental impact than Sc-2 due to decreased electricity consumption and increased bioethanol and xylonic acid outputs. This study highlights the pivotal role of pretreatment methods in wood-based biorefineries and underscores the urgency of sustainable alternatives like TsOH/pentanol. Additionally, adopting greener electricity generation, advanced technologies, and process optimization are crucial for reducing the environmental footprint of waste-based biorefineries while preserving valuable bioproduct production.

Driving sustainable circular economy in electronics: A comprehensive review on environmental life cycle assessment of e-waste recycling.

E-waste, encompassing discarded materials from outdated electronic equipment, often ends up intermixed with municipal solid waste, leading to improper disposal through burial and incineration. This improper handling releases hazardous substances into water, soil, and air, posing significant risks to ecosystems and human health, ultimately entering the food chain and water supply. Formal e-waste recycling, guided by circular economy models and zero-discharge principles, offers potential solutions to this critical challenge. However, implementing a circular economy for e-waste management due to chemical and energy consumption may cause environmental impacts. Consequently, advanced sustainability assessment tools, such as Life Cycle Assessment (LCA), have been applied to investigate e-waste management strategies. While LCA is a standardized methodology, researchers have employed various routes for environmental assessment of different e-waste management methods. However, to the authors' knowledge, there lacks a comprehensive study focusing on LCA studies to discern the opportunities and limitations of this method in formal e-waste management strategies. Hence, this review aims to survey the existing literature on the LCA of e-waste management under a circular economy, shedding light on the current state of research, identifying research gaps, and proposing future research directions. It first explains various methods of managing e-waste in the circular economy. This review then evaluates and scrutinizes the LCA approach in implementing the circular bioeconomy for e-waste management. Finally, it proposes frameworks and procedures to enhance the applicability of the LCA method to future e-waste management research. The literature on the LCA of e-waste management reveals a wide variation in implementing LCA in formal e-waste management, resulting in diverse results and findings in this field. This paper underscores that LCA can pinpoint the environmental hotspots for various pathways of formal e-waste recycling, particularly focusing on metals. It can help address these concerns and achieve greater sustainability in e-waste recycling, especially in pyrometallurgical and hydrometallurgical pathways. The recovery of high-value metals is more environmentally justified compared to other metals. However, biometallurgical pathways remain limited in terms of environmental studies. Despite the potential for recycling e-waste into plastic or glass, there is a dearth of robust background in LCA studies within this sector. This review concludes that LCA can offer valuable insights for decision-making and policy processes on e-waste management, promoting environmentally sound e-waste recycling practices. However, the accuracy of LCA results in e-waste recycling, owing to data requirements, subjectivity, impact category weighting, and other factors, remains debatable, emphasizing the need for more uncertainty analysis in this field.

Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems.

Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.

Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning.

Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R2. Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.

Production of chemicals and utilities in-house improves the environmental sustainability of phytoplankton-based biorefinery.

Life cycle assessment was used to evaluate the environmental impacts of phytoplanktonic biofuels as possible sustainable alternatives to fossil fuels. Three scenarios were examined for converting planktonic biomass into higher-value commodities and energy streams using the alga Scenedesmus sp. and the cyanobacterium Arthrospira sp. as the species of interest. The first scenario (Sc-1) involved the production of biodiesel and glycerol from the planktonic biomass. In the second scenario (Sc-2), biodiesel and glycerol were generated from the planktonic biomass, and biogas was produced from the residual biomass. The process also involved using a catalyst derived from snail shells for biodiesel production. The third scenario (Sc-3) was similar to Sc-2 but converted CO2 from the biogas upgrading to methanol, which was then used in synthesizing biodiesel. The results indicated that Sc-2 and Sc-3 had a reduced potential (up to 60 % less) for damaging human health compared to Sc-1. Sc-2 and Sc-3 had up to 61 % less environmental impact than Sc-1. Sc-2 and Sc-3 reduced the total cumulative exergy demand by up to 44 % compared to Sc-1. In conclusion, producing chemicals and utilities within the biorefinery could significantly improve environmental sustainability, reduce waste, and diversify revenue streams.

Environmental assessment of optimized renewable energy-based microgrids integrated desalination plant: considering human health, ecosystem quality, climate change, and resources.

Using hybrid renewable energy technology is an efficient method for greenhouse gas mitigation caused by fossil fuel combustion. However, these renewable microgrids are not free from environmental damages, especially during the lifetime of hybrid renewable energy systems (HRES). The main objective of this study is to assess the environmental impacts of three optimized HRES for the Sea Water Reverse Osmosis Desalination (SWROD) plant. An objective optimization was developed using the division algorithm, and the environmental impacts of the optimized HRES were investigated by the life cycle assessment approach. The results showed that producing 1 m3 freshwater by an optimal size SWROD integrated with wind turbine/battery is responsible for 3.56E - 07 disability-adjusted life year (DALY). It is significantly less than 1 m3 freshwater production by an optimal size SWROD integrated with solar PV/battery (5.88E - 07 DALY) and solar PV/wind turbine/battery (5.13E - 07 DALY) energy systems. Moreover, 1 m3 freshwater by a SWROD integrated with proposed microgrids in this study led to a damage of 0.089 to 0.193 potentially disappeared fraction of species (PDF)*m2*yr to ecosystem quality. It also results in an emission of 0.143 to 0.339 kg CO2 eq per 1 m3 freshwater. Furthermore, resources for 1 m3 freshwater production by a SWROD are calculated at 2.77 to 4.806 MJ primary. Freshwater production by an optimal size SWROD integrated with solar wind/battery compared with solar PV/battery and solar PV/wind turbine/battery had less damage to ecosystem quality, climate, and resources. The results showed reductions of 91.23% in human health, 73.51% in an ecosystem quality, 92.43% in climate change, and 90.08% in resources for producing 1 m3 of freshwater using SWROD integrated with wind turbine/battery bank compared to fossil-based desalination. Finally, the result showed that solving the optimization problem using the division algorithm compared to other algorithms leads to less environmental damage in freshwater production.

Managing the hazardous waste cooking oil by conversion into bioenergy through the application of waste-derived green catalysts: A review.

Waste cooking oil (WCO) is a hazardous waste generated at staggering values globally. WCO disposal into various ecosystems, including soil and water, could result in severe environmental consequences. On the other hand, mismanagement of this hazardous waste could also be translated into the loss of resources given its energy content. Hence, finding cost-effective and eco-friendly alternative pathways for simultaneous management and valorization of WCO, such as conversion into biodiesel, has been widely sought. Due to its low toxicity, high biodegradability, renewability, and the possibility of direct use in diesel engines, biodiesel is a promising alternative to mineral diesel. However, the conventional homogeneous or heterogeneous catalysts used in the biodiesel production process, i.e., transesterification, are generally toxic and derived from non-renewable resources. Therefore, to boost the sustainability features of the process, the development of catalysts derived from renewable waste-oriented resources is of significant importance. In light of the above, the present work aims to review and critically discuss the hazardous WCO application for bioenergy production. Moreover, various waste-oriented catalysts used to valorize this waste are presented and discussed.

Safflower-based biorefinery producing a broad spectrum of biofuels and biochemicals: A life cycle assessment perspective.

Global environmental awareness has encouraged further research towards biofuel production and consumption. Despite the favorable properties of biofuels, the sustainability of their conventional production pathways from agricultural feedstocks has been questioned. Therefore, the use of non-food feedstocks as a promising approach to ensure sustainable biofuel production is encouraged. However, the use of synthetic solvents/chemicals and energy carriers during biofuel production and the consequent adverse environmental effects are still challenging. On the other hand, biofuel production is also associated with generating large volumes of waste and wastewater. Accordingly, the circular bioeconomy as an innovative approach to ensure complete valorization of feedstocks and generated waste streams under the biorefinery scheme is proposed. In line with that, the current study aims to assess the environmental sustainability of bioethanol production in a safflower-based biorefinery using the life cycle assessment framework. Based on the obtained results, safflower production and its processing into 1 MJ bioethanol under the safflower-based biorefinery led to damage of 2.23E-07 disability-adjusted life years (DALY), 2.35E-02 potentially disappeared fraction (PDF)*m2*yr, 4.76E-01 kg CO2 eq., and 3.82 MJ primary on the human health, ecosystem quality, climate change, and resources, respectively. Moreover, it was revealed that despite adverse environmental effects associated with safflower production and processing, the substitution of conventional products, i.e., products that are the typical products in the market without having environmental criteria, with their bio-counterparts, i.e., products produced in the biorefinery based on environmental criteria could overshadow the unfavorable effects and substantially enhance the overall sustainability of the biorefinery system. The developed safflower-based biorefinery led to seven- and two-time reduction in damage to the ecosystem quality and resources damage categories, respectively. The reductions in damage to human health and climate change were also found to be 52% and 24%, respectively. The weighted environmental impacts of the safflower-based biorefinery decreased by 64% due to the production of bioproducts, mainly biodiesel and biogas, replacing their fossil-based counterparts, i.e., diesel and natural gas, respectively. Finally, although the main focus of the developed safflower-based biorefinery was biofuel production, waste valorization and mainly animal feed played a significant role in improving the associated environmental impacts.

Exergetic performance evaluation of a diesel engine powered by diesel/biodiesel mixtures containing oxygenated additive ethylene glycol diacetate.

A diesel engine running on diesel/biodiesel mixtures containing ethylene glycol diacetate (EGDA) was investigated from the exergoeconomic and exergoenvironmental viewpoints. Biodiesel was mixed with petrodiesel at 5% and 20% volume ratios, and the resultant mixtures were then doped with EGDA at 1-3% volume ratios. The exergetic sustainability indicators of the engine operating on the prepared fuel formulations were determined at varying engine loads. The indicators were selected to support decision-making on fuel composition and engine load following thermodynamic, economic, and environmental considerations. The engine load markedly affected all the studied exergetic parameters. The highest engine exergetic efficiency (39.5%) was obtained for petrodiesel doped with 1 v/v% EGDA at the engine load of 50%. The minimum value of the unit cost of brake power exergy (49.6 US$/GJ) was found for straight petrodiesel at full-load conditions, while the minimum value of the unit environmental impact of brake power exergy (29.9 mPts/GJ) was observed for petrodiesel mixed with 5 v/v% biodiesel at the engine load of 75%. Overall, adding EGDA to fuel mixtures did not favorably influence the outcomes of both exergetic methods due to its energy-intensive and cost-prohibitive production process. In conclusion, although petrodiesel fuel improvers such EGDA used in the present study could properly mitigate pollutant emissions, the adverse effects of such additives on thermodynamic parameters of diesel engines, particularly on exergoeconomic and exergoenvironmental indices, need to be taken into account, and necessary optimizations should be made before their real-world application.

Exergy intensity and environmental consequences of the medical face masks curtailing the COVID-19 pandemic: Malign bodyguard?

On January 30, 2020, the World Health Organization identified SARS-CoV-2 as a public health emergency of global concern. Accordingly, the demand for personal protective equipment (PPE), including medical face masks, has sharply risen compared with 2019. The new situation has led to a sharp increase in energy demand and the environmental impacts associated with these product systems. Hence, the pandemic's effects on the environmental consequences of various PPE types, such as medical face masks, should be assessed. In light of that, the current study aimed to identify the environmental hot-spots of medical face mask production and consumption by using life cycle assessment (LCA) and tried to provide solutions to mitigate the adverse impacts. Based on the results obtained, in 2020, medical face masks production using fossil-based plastics causes the loss of 2.03 × 103 disability-adjusted life years (DALYs); 1.63 × 108 PDF*m2*yr damage to ecosystem quality; the climate-damaging release of 2.13 × 109 kg CO2eq; and 5.65 × 1010 MJ damage to resources. Besides, annual medical face mask production results in 5.88 × 104 TJ demand for exergy. On the other hand, if used makes are not appropriately handled, they can lead to 4.99 × 105 Pt/yr additional damage to the environment in 2020 as determined by the EDIP 2003. Replacement of fossil-based plastics with bio-based plastics, at rates ranging from 10 to 100%, could mitigate the product's total yearly environmental damage by 4-43%, respectively. Our study calls attention to the environmental sustainability of PPE used to prevent virus transmission in the current and future pandemics.

Improving sustainability and mitigating environmental impacts of agro-biowaste compost fertilizer by pelletizing-drying.

The use of agro-biowaste compost fertilizers in agriculture is beneficial from technical, financial, and environmental perspectives. Nevertheless, the physical, mechanical, and agronomical attributes of agro-biowaste compost fertilizers should be engineered to reduce their storage, handling, and utilization costs and environmental impacts. Pelletizing and drying are promising techniques to achieve these goals. In the present work, the effects of process parameters, including compost particle size/moisture content, pelletizing compression ratio, and drying air temperature/velocity, were investigated on the density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet. The Taguchi technique was applied to understand the effects of independent parameters on the output responses, while the optimal pellet properties were found using the iterative thresholding method. The soil and plant (sweet basil) response to the optimal biocompost pellet was experimentally evaluated. The farm application of the optimal pellet was also compared with the untreated agro-biowaste compost using the life cycle assessment approach to investigate the potential environmental impact mitigation of the pelletizing and drying processes. Generally, the compost moisture content was the most influential factor on the density and specific crushing energy of the dried pellet, while the moisture diffusion of the wet pellet during the drying process was significantly influenced by the pelletizing compression ratio. The density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet at the optimal conditions were 1242.49 kg/m3, 0.5054 MJ/t, and 8.2 × 10-8 m2/s, respectively. The optimal biocompost pellet could release 80% of its nitrogen content evenly over 98 days, while this value was 28 days for the chemical urea fertilizer. Besides, the optimal pellet could significantly improve the agronomical attributes of the sweet basil plant compared with the untreated biocompost. The applied strategy could collectively mitigate the weighted environmental impact of farm application of the agro-biowaste compost by more than 63%. This reduction could be attributed to the fact that the pelletizing-drying processes could avoid methane emissions from the untreated agro-biowaste compost during the farm application. Overall, pelletizing-drying of the agro-biowaste compost could be regarded as a promising strategy to improve the environmental and agronomical performance of farm application of organic biofertilizers.

Exergy analysis of a whole-crop safflower biorefinery: A step towards reducing agricultural wastes in a sustainable manner.

The huge amount of agro-wastes generated due to expanding agricultural activities can potentially cause serious environmental and human health problems. Using the biorefinery concept, all parts of agricultural plants can be converted into multiple value-added bioproducts while reducing waste generation. This approach can be viewed as an effective strategy in developing and realizing a circular bioeconomy by accomplishing the dual goals of waste mitigation and energy recovery. However, the sustainability issue of biorefineries should still be thoroughly scrutinized using comprehensive resource accounting methods such as exergy-based approaches. In light of that, this study aims to conduct a detailed exergy analysis of whole-crop safflower biorefinery consisting of six units, i.e., straw handling, biomass pretreatment, bioethanol production, wastewater treatment, oil extraction, and biodiesel production. The analysis is carried out to find the major exergy sink in the developed biorefinery and discover the bottlenecks for further performance improvements. Overall, the wastewater treatment unit exhibits to be the major exergy sink, amounting to over 70% of the total thermodynamic irreversibility of the process. The biomass pretreatment and bioethanol production units account for 12.4 and 10.3% of the total thermodynamic inefficiencies of the process, respectively. The exergy rates associated with bioethanol, biodiesel, lignin, biogas, liquid digestate, seed cake, sodium sulfate, and glycerol are determined to be 5918.5, 16516.8, 10778.9, 1741.4, 6271.5, 15755.8, 3.4, and 823.5 kW, respectively. The overall exergetic efficiency of the system stands at 72.7%, demonstrating the adequacy of the developed biorefinery from the thermodynamic perspective.

Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel.

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/ diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 µM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE < 5.70 × 10-3, RMSE < 7.55 × 10-2, and MAPE < 3.86 × 10-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 µM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations were found to be 7.26 vol% , 0.46 vol% , 95.7  ppm, and 36.2 ppm, respectively, under the selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level ( 34.0 %). In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Data supporting consolidating emission indices of a diesel engine powered by carbon nanoparticle-doped diesel/biodiesel emulsion fuels using life cycle assessment framework.

Integrated environmental analysis using life cycle assessment for different fuel blends used in a single-cylinder diesel engine was performed to select the most eco-friendly fuel blend. More specifically, the inventory data in support of the integrated environmental analysis of water-emulsified 5% biodiesel/diesel blends (B5) containing different levels of carbon nanoparticles (i.e., 38, 75, and 150 µM) as a novel fuel nanoadditives at a fixed engine speed of 1000 rpm and four different engine loads (i.e., 25, 50, 75, and 100%) are presented. Neat diesel, B5, and B5 containing water (3 wt.%) were used as controls. Raw data related to the production and combustion of fuel blends were experimentally collected. Industrial (i.e., experiments at large scale) and laboratory (i.e., experiments at small scale) data were used for fuel blends production while experimental data obtained by engine tests were used for the combustion stage. Then raw data were processed with the IMPACT 2002+ methods by using the SimaPro software and EcoInvent database and were then converted into environmental impacts. Accordingly, six supplementary files including the inventory data on integrated environmental analysis of the different fuel blends are presented (Supplementary Files 1-6). The data could be applied for integrated environmental analysis in order to avoid subjective weighting of combustion parameters for selecting the most eco-friendly fuel blend for use in diesel engines. More specifically, by developing a single score indicator obtained through conducting integrated combustion analysis, comparison of various fuel blends is largely facilitated.

Life-Cycle Assessment (LCA) Analysis of Algal Fuels.

Life-cycle assessment (LCA) is one of the most attractive tools employed nowadays by environmental policy-makers as well as business decision-makers to ensure environmentally sustainable production/consumption of various goods/services. LCA is a systematic, rigorous, and standardized approach aimed at quantifying resources consumed/depleted, pollutants released, and the related environmental and health impacts through the course of consumption and production of goods/service. Algal fuels are no exception and their environmental sustainability could be well scrutinized using the LCA methodology. In line with that, this chapter is devoted to present guidelines on the technical aspects of LCA application in algal fuels while elaborating on major standards used, i.e., ISO 14040 and 14044 standards. Overall, LCA practitioners as well as technical experts dealing with algal fuels in both the public and private sectors could be the main target audience for these guidelines.

Data on environmental analysis of natural antioxidant production from walnut husk by a solar photovoltaic-driven system as a replacement for potentially carcinogenic synthetic antioxidants.

In order to develop a product sustainably, multiple analyses, including comprehensive environmental assessment, are required. Solar-assisted production of walnut husk methanolic extract (WHME) as a natural antioxidant for biodiesel was scrutinized by using the life cycle assessment (LCA) approach. More specifically, the environmental sustainability of WHME antioxidant was evaluated and compared to that of propyl gallate (PG), the most widely used synthetic biodiesel antioxidant, under two scenarios. Additionally, supplementary files including the inventory data consisting of raw data as well as elementary flows, mid-point, and end-point categories are presented. The analysis of scenarios revealed that the use of the natural antioxidant and the avoidance of the chemical antioxidant in biodiesel fuel could be regarded as an eco-friendly approach substantially enhancing the environmental friendliness of biodiesel in particular in terms of human health. Furthermore, given the waste-oriented nature of WHME, the scenario involved its application could serve as a promising strategy to simultaneously valorize the agro-waste and generate a value-added product; a move toward implementing the circular economy approach in the biodiesel industry.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg-1 and 66,112.94MJkg-1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301 MJ per ton of dry tea, respectively.