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The present study utilizes (a) recycled low density polyethylene oil extracted through catalytic pyrolysis and (b) 1-decanol and di-n-butyl ether, as a blend component with diesel to power a single...

Natural ecosystems benefit human communities by providing ecosystem services such as water purification and crop pollination. Mapping ecosystem service values has become popular, but most are static snapshots of average value. Estimating instead the economic impacts of specific ecosystem changes can better inform typical resource decisions. Here we develop an approach to mapping marginal values, those resulting from the next unit of ecosystem change, across landscapes. We demonstrate the approach with a recent model of crop pollination services in Costa Rica, simulating deforestation events to predict resulting marginal changes in pollination services to coffee farms. We find that marginal losses from deforestation vary from zero to US$700/ha across the landscape. Financial risks for farmers from these losses and marginal benefits of forest restoration show similar spatial variation. Marginal values are concentrated in relatively few forest parcels not identified using average value. These parcels lack substitutes: nearby forest parcels that can supply services in the event of loss. Indeed, the marginal value of forest parcels declines exponentially with the density of surrounding forest cover. The approach we develop is applicable to any ecosystem service. Combined with information on costs, it can help target conservation or restoration efforts to optimize benefits to people and biodiversity.

Abstract Analytical solutions for superadiabatic filtration combustion of lean methane-air mixtures in a monolithic porous burner are sought. The one-dimensional, local volume-averaged equations of energy and species conservation that assume a non-thermal equilibrium (i.e., the two-medium treatment), are converted by a coordinate transformation using a combustion wave speed and solved to obtain close-form solutions. A parametric examination varying inlet gas velocity, fuel equivalence ratio, porosity, and thermal conductivity and diffusivity of the solid phase of the porous burner proves the validity of the analytical solutions which are in an excellent agreement with the numerical benchmark. The analytical solutions depict the key features of the filtration combustion such as non-thermal equilibrium between the solid and gas phases, superadiabatic flame temperature, and internal heat recirculation between solid and gas phases of the porous burner.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).

A reduction of the inactive components can increase the energy density and reduce production cost of Li‐ion batteries. But an effective reduction of the binder amount also negatively affects the adhesion of the electrode. Herein, slot‐die coating of a primer layer for Li‐ion anodes is investigated. It is shown that the use of a primer layer with only 0.3 g m−2 can increase the adhesive force by the factor of 5 as well as the cell performance for anodes with low binder content. The process limits for a stable, defect‐free primer coating are investigated at coating speeds of up to 550 m min−1. The limits coincide both for a setup without vacuum box and with vacuum box with theory‐based equations. By using a vacuum box, the minimum wet film thickness can be reduced by half.

Carbon capture and utilization (CCU) is a field of key emerging technologies. CCU can support the economy to decrease the dependency on fossil carbon raw materials, to stabilize electricity grids and markets with respect to a growing share of fluctuating renewable energy. Furthermore, it can contribute to mitigate anthropogenic CO2 emissions. The German Federal Ministry of Education and Research has provided substantial financial support for research and development projects, stimulating research, development, and innovations in the field of CO2 utilization. This review provides an overview over the most relevant funding measures in this field. Examples of successful projects demonstrate that CCU technologies are already economically viable or technologically ready for industrial application. CCU technologies as elements of a future "green economy" can contribute to reach the ambitious German sustainability targets with regard to climate protection as well as raw material productivity.

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

In the UK electricity market, generators are obliged to produce part of their electricity with renewable energy resources in accordance with the Renewable Obligation Order. Since 2009 technology banding has been added, meaning that different technologies are rewarded with a different number of certificates. We analyze these two different renewable obligation policies in a mathematical representation of an electricity market with random availabilities of renewable generation outputs and random electricity demand. We also present another, alternative, banding policy. We provide revenue adequate pricing schemes for the three obligation policies. We carry out a simulation study via sampling. A key finding is that the UK banding policy cannot guarantee that the original obligation target is met, hence potentially resulting in more pollution. Our alternative provides a way to make sure that the target is met while supporting less established technologies, but it comes with a significantly higher consumer price. Furthermore, as an undesirable side effect, we observe that a cost reduction in a technology with a high banding (namely offshore wind) leads to more CO2 emissions under the UK banding policy and to higher consumer prices under the alternative banding policy.