Feasibility Study and Simulation of Utilization of Renewable Energies in a Broiler Industry

This research was conducted to feasibility study and simulation of utilization of renewable energies (solar and biomass) in broiler industry in Khorramabad County. Data was collected by field sampling (for a breeding period in winter 2015-2016) and from organizations. In the simulation of a grid-connected photovoltaic system (20 kW) with PVsyst 6.7 software, the average performance ratio and available useful energy of the system were calculated at 0.785 and 4.75 kWh/kWp/day, respectively. The use of photovoltaic system can cover 25% of electrical energy in broiler production farms in winter season. Also, in feasibility study of combined heat and power system, the potential of biogas production from broiler manure was calculated at 448.5 m3 per 1000 pieces of broiler. The use of biogas plant can supply 98% of the electrical energy of broiler production farms in winter season. According to the results, the use of renewable energies in the present conditions, despite the reduction of fossil fuels consumption and many environmental benefits, in the broiler industry is not economical

Life cycle environmental sustainability and energy assessment of timber wall construction : a comprehensive overview

This article presents a comprehensive overview of the life cycle environmental and energy assessment for all residential and commercial constructions made of timber walls, globally. The study was carried out based on a systematic literature analysis conducted on the Scopus database. A total of 66 research articles were relevant to timber wall design. Among these, the residential construction sector received more attention than the commercial sector, while the low-rise construction (1–2 stories) gained more attention than high-rise construction (>5 stories). Most of these studies were conducted in Canada, Europe, Malaysia, and the USA. In addition, the end-of-life phase received limited attention compared to upstream phases in most of the studies. We compared all environmental and energy-based life cycle impacts that used “m2” as the functional unit; this group represented 21 research articles. Global warming potential was understandably the most studied life cycle environmental impact category followed by acidification, eutrophication, embodied energy, photochemical oxidation, and abiotic depletion. In terms of global warming impact, the external walls of low-rise buildings emit 18 to 702 kg CO2 kg eq./m2, while the internal walls of the same emit 11 kg CO2 kg eq./m2. In turn, the walls of high-rise buildings carry 114.3 to 227.3 kg CO2 kg eq./m2 in terms of global warming impact. The review highlights variations in timber wall designs and the environmental impact of these variations, together with different system boundaries and varying building lifetimes, as covered in various articles. Finally, a few recommendations have been offered at the end of the article for future researchers of this domain

Review of Latest Advances and Prospects of Energy Storage Systems: Considering Economic, Reliability, Sizing, and Environmental Impacts Approach

Studies have shown that the role of energy storage systems in human life is increasing day by day. Therefore, this research aims to study the latest progress and technologies used to produce energy storage systems. It also discusses and compares the most recent methods used by researchers to model and optimize the size of these tools and evaluates the strengths and weaknesses of each. Investigations have shown that using energy storage systems in hybrid stand-alone power generation systems based on renewable energy increases the reliability of the power generation systems and increases their efficiency. It has also reduced the cost of transmitting the power grid to remote areas. Furthermore, this study showed that advances in energy storage technology in recent years have led to the development and promotion of clean microgrids. In addition, this review paper also addresses energy storage technology issues and proposes practical and applied solutions

A comprehensive assessment of the environmental footprint of pomegranate juice production system by life cycle assessment approach

This groundbreaking research conducts the first comprehensive life cycle assessment of pomegranate juice production system, identifying key environmental hotspots, and offering valuable insights for improving environmental performance. This study employs the ReCiPe method within SimaPro software to assess the environmental impacts per pack of 160 g of juice. The findings indicated that a pack of pomegranate juice leads to 0.15 kg CO2 eq. Among all factors affecting global warming, natural gas used for juicing stood out as the most significant, responsible for over one-third of the impacts. In addition, the amount of human carcinogenic and non-carcinogenic toxicity impact categories was equal to 0.004 and 0.063 kg 1,4-DCB, respectively, mainly due to the pomegranate production in the orchard phase. Fruit production is also an environmental hotspot in other impact categories. The emissions from chemical fertilizers and manure application, fruit transportation toward the factory, and diesel fuel consumption by machinery were recognized to be the main hotspots in the orchard. Based on the weighted damage categories, the production of one pack of juice led to 3.64 mPt damage to the environment. The damage to human health contributed to 88% of the weighted environmental damage. The outcome of the sensitivity analysis indicated that natural gas in the juicing process is the most important parameter in damage to human health, ecosystems, and resource. In line with that, efforts for optimized consumption of natural gas and its replacement with eco-friendly alternatives can be a promising approach in pomegranate juice production from the environmental point of view

Review of Latest Advances and Prospects of Energy Storage Systems: Considering Economic, Reliability, Sizing, and Environmental Impacts Approach

Studies have shown that the role of energy storage systems in human life is increasing day by day. Therefore, this research aims to study the latest progress and technologies used to produce energy storage systems. It also discusses and compares the most recent methods used by researchers to model and optimize the size of these tools and evaluates the strengths and weaknesses of each. Investigations have shown that using energy storage systems in hybrid stand-alone power generation systems based on renewable energy increases the reliability of the power generation systems and increases their efficiency. It has also reduced the cost of transmitting the power grid to remote areas. Furthermore, this study showed that advances in energy storage technology in recent years have led to the development and promotion of clean microgrids. In addition, this review paper also addresses energy storage technology issues and proposes practical and applied solutions

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

Optimization of energy consumption of dairy farms using data envelopment analysis – A case study: Qazvin city of Iran

The aim of this study was to use the data envelopment analysis for determining the energy efficiency and find the optimum energy consumption in dairy farms of Qazvin city of Iran. In this study have been used from two approaches constant returns to scale and variable returns to scale model of data envelopment analysis for determining the degrees of technical efficiency, pure technical efficiency and scale efficiency. Moreover, the effect of optimum energy consumption on greenhouse gas emissions has been studied and also the total amount of greenhouse gas emissions. The results showed that from total number of dairy farms 42.55% and 53.19% were efficient based on constant returns to scale and variable returns to scale model, respectively. Accordingly, the average score of technical, pure technical and scale efficiencies of farmers were calculated 0.9, 0.94 and 0.953, respectively. The total optimum energy required was estimated 129,932 (MJ cow−1). Energy saving target ratio for dairy farms was calculated as 12%. According to results feed intake had the highest share (85.44%) from total saving energy, followed by fossil fuels (11.19%). The total greenhouse gas emission was calculated as 5393 (kgCO2eq. cow−1 year−1) in dairy farms that this amount can be reduced to 4738 (kgCO2eq. cow−1 year−1) with optimum energy consumption. The enteric fermentation had the highest potential to reduction of total GHG emissions with 47% that has a direct connection to the amount of feed intake. Keywords: Data envelopment analysis, Dairy farm, Energy, Greenhouse gas emission, Optimizatio

Machine learning technology in biodiesel research: A review

Biodiesel has the potential to significantly contribute to making transportation fuels more sustainable. Due to the complexity and nonlinearity of processes for biodiesel production and use, fast and accurate modeling tools are required for their design, optimization, monitoring, and control. Data-driven machine learning (ML) techniques have demonstrated superior predictive capability compared to conventional methods for modeling such highly complex processes. Among the available ML techniques, the artificial neural network (ANN) technology is the most widely used approach in biodiesel research. The ANN approach is a computational learning method that mimics the human brain's neurological processing ability to map input-output relationships of ill-defined systems. Given its high generalization capacity, ANN has gained popularity in dealing with complex nonlinear real-world engineering and scientific problems. This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research. Moreover, the advantages and disadvantages of using ML technology in biodiesel research are highlighted to direct future R&D efforts in this domain. ML technology has generally been used in biodiesel research for modeling (trans)esterification processes, physico-chemical characteristics of biodiesel, and biodiesel-fueled internal combustion engines. The primary purpose of introducing ML technology to the biodiesel industry has been to monitor and control biodiesel systems in real-time; however, these issues have rarely been explored in the literature. Therefore, future studies appear to be directed towards the use of ML techniques for real-time process monitoring and control of biodiesel systems to enhance production efficiency, economic viability, and environmental sustainability