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This paper explores the effects of various parameters on the triticale straw cutting process and the subsequent biomass yield. The impact of blade angle (α), rake angle (β), and the distance between the blade and counter-blade (g) were examined in relation to the shaft torque (T d) on the cutting knife drum, the energy consumption (E c), and the required power (P) for processing straw. Heterogeneity among straw samples was taken into account to ensure accurate calibration of responses. An analysis of variance (ANOVA) was employed to investigate the interactions between input variables and recorded responses, leading to the identification of optimal cutting parameters (α = 35° and β = 0°) that minimise T d , E c , and P. Additionally, the components of the recorded torque T d , including cutting torque (T c) and friction torque (T f), were analysed. Parameter f was used to ascertain the percentage of T f in T d , enabling an optimisation process that confirmed the experimental results and pinpointed the parameter values that resulted in the least energy-intensive process. • ANOVA analysis of influence of process settings on the cutting process results. • The knife blade angle has the most significant influence on energy consumption. • The distance between blade and counter-blade has less influence on energy consumption. • The share of friction torque in the cutting torque facilitated numerical optimisation. • Lower energy consumption of cutting improves energy balance of biofuel production. [ABSTRACT FROM AUTHOR]

Combined heat and power (CHP) plants fueled by biomass can collect and utilize waste straw resources in a productive way. This paper considers the impact of regional factors on biomass energy potential and the energy needs of the population, so as to study the differences in construction of biomass CHP plants and the collection scope of raw materials, and proposes evaluating suitability for biomass energy development based on scope of resource collection. Taking five counties in China as its study areas, this paper assesses biomass energy potential. A topology system of biomass CHP plants has been reasonably established in different counties through ArcGIS, the required installed capacity has been calculated according to the number of persons served by such plants. Finally, the collection length and corresponding value range of raw materials of CHP plants along roads has been obtained based on biomass energy potential and energy demand. The result shows that the differences in area, straw yield and biomass fuelization rate depending on regions have a great impact on biomass energy potential, while the residue-to-product ratio of straw and biomass calorific value have less of an impact. When the biomass energy per capita of a region reaches 9.75GJ/person, it is suitable for biomass energy development. The installed capacity in the biomass CHP plant system of each study area is mostly within the scope of 3–59 MW, and the collection length of corresponding biomass resources of such plants along roads is mostly within the scope of 5.09–25.23 km. • Assessment of biomass energy potential based on regional biomass differences. • Suitability for biomass energy development based on scope of resource collection. • Strategies for biomass plant system construction in different regions are proposed. [ABSTRACT FROM AUTHOR]

In the global effort to reduce CO 2 emissions, the concurrent enhancement of pollutant degradation and reductions in fossil fuel consumption are pivotal aspects of microalgae-mediated wastewater treatment. Clarifying the degradation mechanisms of bacteria and microalgae during pollutant treatment, as well as regulatory biolipid production, could enhance process sustainability. The synergistic and inhibitory relationships between microalgae and bacteria are introduced in this paper. The different stimulators that can regulate microalgal biolipid accumulation are also reviewed. Wastewater treatment technologies that utilize microalgae and bacteria in laboratories and open ponds are described to outline their application in treating heavy metal-containing wastewater, animal husbandry wastewater, pharmaceutical wastewater, and textile dye wastewater. Finally, the major requirements to scale up the cascade utilization of biomass and energy recovery are summarized to improve the development of biological wastewater treatment. [Display omitted] • The synergistic and antagonistic interactions between bacteria and microalgae are summarized. • Different stimulators that could regulate microalgal biolipid accumulation are also reviewed. • Wastewater treatment technologies that utilize microalgae and bacteria in the laboratory and open ponds are described. • The trends in microalgae-bacterial consortium in multistage energy production are proposed. [ABSTRACT FROM AUTHOR]

Cloud computing offers attractive features for both service providers and customers. Users benefit from the pay-as-you-go model by saving expenditures and service providers are deploying their services to cloud data centers to reduce their maintenance efforts. However, due to the fast growth of cloud data centers, the energy consumed by the data centers can lead to a huge amount of carbon emission with environmental impacts, and the carbon intensity of different locations are varied among different power plants according to the sources of energy. Thus, in this paper, to address the carbon emission problem of data centers, we consider shifting the workloads among multi-cloud located in different time zones. We also formulate the energy usage and carbon emission of data centers and model the solar power corresponding to the locations. This helps to reduce the usage of brown energy and maximize the utilization of renewable energy at different locations. We propose an approach for managing carbon footprint and renewable energy for multiple data centers at California, Virginia, and Dublin, which are in different time zones. The results show that our proposed approaches that apply workload shifting can reduce around 40% carbon emission in comparison to the baseline while ensuring the average response time of user requests. • Model considering power consumption, renewable energy and carbon footprint. • Algorithm for managing the renewable energy and carbon footprint for data centers. • Evaluations on the impacts of requests executed in different time zones. [ABSTRACT FROM AUTHOR]

Low-carbon transition in the non-electric energy sector, which includes transport and heating energy, is necessary for achieving the 2 °C target. Meanwhile, as non-electric energy accounts for over 60% of total water consumption in the energy supply sector, it is vital to understand future water trends in the context of decarbonization. However, few studies have focused on life-cycle water impacts for non-electric energy; besides, applying conventional LCA methodology to assess non-electric energy has limitations. In this paper, a Multi-Regional Hybrid Life-Cycle Assessment (MRHLCA) model is built to assess total CO 2 emissions and water consumption of 6 non-electric energy technologies – transport energy from biofuel and gasoline, heat supply from natural gas, biogas, coal, and residual biomass, within 7 major emitting economies. We find that a shift to natural gas and residual biomass heating can help economies reduce 14–65% CO 2 and save more than 21% water. However, developed and developing economies should take differentiated technical strategies. Then we apply scenarios from IMAGE model to demonstrate that if economies take cost-effective 2 °C pathways, the water conservation synergy for the whole energy supply sector, including electricity, can also be achieved. [ABSTRACT FROM AUTHOR]

Ever-growing demands for energy, the unsustainability of fossil fuel due to its scarcity and massive impact on global economies and the environment, have encouraged the research on alternative power sources to work upon for the governments, companies, and scientists across the world. Enzymatic biofuel cells (eBFCs) is one category of fuel cell that can harvest energy from biological moieties and has the future to be used as an alternative source of energy. The aim of this review is to summarize the background and state-of-the-art in the field of eBFCs. This review article will be very beneficial for a wide audience including students and new researchers in the field. A part of the paper summarized the challenges in the preparation of anode and cathode and the involvement of nanomaterials and conducting polymers to construct the effective bioelectrodes. It will provide an insight for the researchers working in this challenging field. Furthermore, various applications of eBFCs in implantable power devices, tiny electronic gadgets, and self powered biosensors are reported. This review article explains the development in the area of eBFCs for several years from its origin to growth systematically. It reveals the strategies that have been taken for the improvements required for the better electrochemical performance and operational stability of eBFCs. It also mentions the challenges in this field that will require proper attention so that the eBFCs can be utilized commercially in the future. The review article is written and structurized in a way so that it can provide a decent background of eBFCs to its reader. It will definitely help in enhancing the interest of reader in eBFCs. [Display omitted] • A general introduction along with the background and advances in the field of eBFCs are explained. • The challenges in the preparation of anode and cathode for the employment in eBFCs are mentioned. • Enzyme immobilization and stabilization at surface of electrode via Physical adsorption, Covalent binding, Entrapment, and Cross-linking are reported with example with their advantages and disadvantages. • Bioelectrode preparation from nanomaterials and conducting polymers are explained. • Summarized the current improvement in eBFCs along with their potential applications in implantable biomedical devices. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO 2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments – through a software capable of emulating a quantum system – to data over the period of 1990–2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany. • An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. • The empirical findings support the powerful role of biomass energy in reducing CO 2 emissions. • GDP represents an important determinant of environmental pollution in Germany. [ABSTRACT FROM AUTHOR]