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Biogas is one of the most attractive renewable resources due to its ability to convert waste into energy. Biogas is produced during an anaerobic digestion process from different organic waste resources with a combination of mainly CH4 (~50 mol/mol), CO2 (~15 mol/mol), and some trace gasses. The percentage of these trace gases is related to operating conditions and feedstocks. Due to the impurities of the trace gases, raw biogas has to be cleaned before use for many applications. Therefore, the cleaning, upgrading, and utilization of biogas has become an important topic that has been widely studied in recent years. In this review, raw biogas components are investigated in relation to feedstock resources. Then, using recent developments, it describes the cleaning methods that have been used to eliminate unwanted components in biogas. Additionally, the upgrading processes are systematically reviewed according to their technology, recovery range, and state of the art methods in this area, regarding obtaining biomethane from biogas. Furthermore, these upgrading methods have been comprehensively reviewed and compared with each other in terms of electricity consumption and methane losses. This comparison revealed that amine scrubbing is one the most promising methods in terms of methane losses and the energy demand of the system. In the section on biogas utilization, raw biogas and biomethane have been assessed with recently available data from the literature according to their usage areas and methods. It seems that biogas can be used as a biofuel to produce energy via CHP and fuel cells with high efficiency. Moreover, it is able to be utilized in an internal combustion engine which reduces exhaust emissions by using biofuels. Lastly, chemical production such as biomethanol, bioethanol, and higher alcohols are in the development stage for utilization of biogas and are discussed in depth. This review reveals that most biogas utilization approaches are in their early stages. The gaps that require further investigations in the field have been identified and highlighted for future research.

Spent coffee grounds (SCG) are industrial biowaste resulting from the coffee-brewing process, and they are often underutilized and end up in landfills, thereby leading to the emission of toxic gases and environmental damage. Hydrothermal carbonization (HTC) is an attractive approach to valorize wet biomass such as SCG to valuable bioproducts (i.e., hydrochar). Thus, in this work, the HTC of SCG was carried out in a 500 L stainless steel vessel at 150, 170, 190, 210, and 230 °C for 30 min, 60 min, 90 min, and 120 min and a feedstock to water weight ratio of 1:5, 1:10, and 1:15, and the use of the resulting hydrochar as a solid fuel was evaluated. The results showed that a high energy recovery (83.93%) and HHV (23.54 MJ/kg) of hydrochar was obtained at moderate conditions (150 °C, 30 min, and feedstock to water weight ratio of 1:5) when compared with conventional approaches such as torrefaction. Following this, the surface morphology, functionality, and combustion behavior of this hydrochar were characterized by SEM, FTIR, and TGA, respectively. In short, it can be concluded that HTC is an effective approach for producing solid fuel from SCG and the resulting hydrochar has the potential to be applied either in domestic heating or large-scale co-firing plants.

The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

This paper proposes a novel peer-to-peer (P2P) decentralized energy market consisting of retailers and prosumers considering integrated demand response (IDR). Retailers can trade electrical energy and gas with prosumers in a P2P way to maximize their welfare. Since they are equipped with electrical storage and power self-generation units, they can benefit from selling power not only to the upstream network but also to prosumers. In peer-to-peer transactions, the prosumers purchase electricity as well as gas from retailers. Because of their access to the competitive retail market, including some retailers, they enjoy more freedom to reduce their energy supply cost. In addition, the prosumers are equipped with an energy hub consisting of combined heat and power (CHP) units and electric pumps, allowing them to change their energy supply according to price fluctuations. Furthermore, they have some changeable electrical and thermal load enabling them to change their load if needed. To clear the proposed P2P decentralized market, a fully decentralized approach called the fully decentralized alternating direction method of multipliers (ADMM) is applied. This method does not require a supervisory entity and, thus, preserves the players’ private information. The numerical studies performed on a system with two retailers and multiple prosumers demonstrate the feasibility and effectiveness of the proposed decentralized market. The results also show that the proposed decentralized algorithm achieves the optimal global solution, compared with the centralized approach.

This article is motivated by a conundrum: How can shale gas development be encouraged and managed without complete knowledge of the associated risks? To answer this question, we used back propagation (BP) neural networks and expert scoring to quantify the relative risks of shale gas development across 12 provinces in China. The results show that the model performs well with high predictive accuracy. Shale gas development risks in the provinces of Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu are relatively high (0.4~0.6), while risks in the provinces of Xinjiang, Guizhou, Yunnan, Anhui, Hunan, Inner Mongolia, and Shanxi are even higher (0.6~1). We make several recommendations based on our findings. First, the Chinese government should promote shale gas development in Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu Provinces, while considering environmental, health, and safety risks by using demonstration zones to test new technologies and tailor China’s regulatory structures to each province. Second, China’s extremely complex geological conditions and resource depths prevent direct application of North American technologies and techniques. We recommend using a risk analysis prioritization method, such as BP neural networks, so that policymakers can quantify the relative risks posed by shale gas development to optimize the allocation of resources, technology and infrastructure development to minimize resource, economic, technical, and environmental risks. Third, other shale gas industry developments emphasize the challenges of including the many parties with different, often conflicting expectations. Government and enterprises must collaboratively collect and share information, develop risk assessments, and consider risk management alternatives to support science-based decision-making with the diverse parties.

Three typical waste furniture boards (fiberboard, chipboard, and blockboard) were pretreated with phosphoric acid and hydrogen peroxide (PHP). The fractionation process of these feedstocks was attempted in order to harvest the cellulose-rich fraction for enzymatic hydrolysis and bioethanol conversion; further, lignin recovery was also considered in this process. The results indicated that 78.9–91.2% of the cellulose was recovered in the cellulose-rich fraction. The decreased crystallinity, which promoted the water retention capacity and enzyme accessibility, contributed greatly to the excellent hydrolysis performance of the cellulose-rich fraction. Therefore, rather high cellulose–glucose conversions of 83.3–98.0% were achieved by hydrolyzing the pretreated furniture boards, which allowed for harvesting 208–241 g of glucose from 1.0 kg of feedstocks. Correspondingly, 8.1–10.4 g/L of ethanol were obtained after 120 h of simultaneous saccharification and fermentation. The harvested lignin exhibited abundant carboxyl –OH groups (0.61–0.67 mmol g−1). In addition, approximately 15–26 g of harvested oligosaccharides were integrated during PHP pretreatment. It was shown that PHP pretreatment is feasible for these highly recalcitrant biomass board materials, which can diversify the bioproducts used in the integrated biorefinery concept.

The objective of this study is to investigate the impacts of the environmental and socio-economic risks on the fisheries in the Mediterranean region from an economic point of view. A balanced panel of 21 Mediterranean countries for 2001–2018 has been estimated by the GLS method, considering heteroskedasticity and correlation among cross sections. The volume of fish landed and landed values have been considered in two models. The results show that increases in sea bottom and surface temperature, H+ ion concentration and salinity threaten the fisheries in the Mediterranean region for the volume of fish landed and that sea surface temperature and salinity negatively influence landed values. In addition, there is an inverse U-shaped relationship between human population and fisheries. Moreover, the Human Development Index (HDI), an indicator of countries’ adaptive capacity, has a positive impact on fisheries and indicates that countries can safeguard fisheries by improving their adaptive capacity. Finally, our results strongly show the risk of climate change for the fisheries in the Mediterranean region and that fisheries are adversely impacted by climate change as well as worsening socio-economic conditions in the absence of adaptation plans.