Search

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models' resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023. [ABSTRACT FROM AUTHOR]

This work investigates the cost-efficient integration of renewable hydrogen into steelworks for the production of methane and methanol as an efficient way to decarbonize the steel industry. Three case studies that utilize a mixture of steelworks off-gases (blast furnace gas, coke oven gas, and basic oxygen furnace gas), which differ on the amount of used off-gases as well as on the end product (methane and/or methanol), are analyzed and evaluated in terms of their economic performance. The most influential cost factors are identified and sensitivity analyses are conducted for different operating and economic parameters. Renewable hydrogen produced by PEM electrolysis is the most expensive component in this scheme and responsible for over 80% of the total costs. Progress in the hydrogen economy (lower electrolyzer capital costs, improved electrolyzer efficiency, and lower electricity prices) is necessary to establish this technology in the future. [ABSTRACT FROM AUTHOR]

To achieve the greenhouse gas reduction targets formulated in the European Green Deal, energy- and resource-intensive industries such as the steel industry will have to adapt or convert their production. In the long term, new technologies are promising. However, carbon capture storage and utilization solutions could be considered as short-term retrofitting solutions for existing steelworks. In this context, this paper presents a first experimental demonstration of an approach to the utilization of process off-gases generated in a steelworks by producing methane and methanol in hydrogen-intensified syntheses. Specifically, the integration of two methane synthesis reactors and one methanol synthesis reactor into a steel plant is experimentally simulated. An innovative monitoring and control tool, namely, a dispatch controller, simulates the process off-gas production using a digital twin of the steel plant and optimizes its distribution to existing and new consumers. The operating states/modes of the three reactors resulting from the optimization problem to be solved by the dispatch controller are distributed in real time via an online OPC UA connection to the corresponding experimental plants or their operators and applied there in a decentralized manner. The live coupling test showed that operating values for the different systems can be distributed in parallel from the dispatch controller to the test rigs via the established communication structure without loss. The calculation of a suitable control strategy is performed with a time resolution of one minute, taking into account the three reactors and the relevant steelworks components. Two of each of the methane/methanol synthesis reactors were operated error-free at one time for 10 and 7 h, respectively, with datasets provided by the dispatch controller. All three reactor systems were able to react quickly and stably to dynamic changes in the load or feed gas composition. Consistently high conversions and yields were achieved with low by-product formation. [ABSTRACT FROM AUTHOR]

Clean energy for all, as listed in the United Nation's SDG7, is a key component for sustainable environmental development. Therefore, it is imperative to uncover the environmental implications of alternative energy technologies. SustainableGAS project simulates different process chains for the substitution of natural gas with renewable energies in the German gas market. The project follows an interdisciplinary approach, taking into account techno-social and environmental variabilities. However, this research highlights the project results from the environmental perspective. So far, a detailed assessment of the environmental costs of alternative gas technologies with a focus on the process of energy transition has remained rare. Although such data constitute key inputs for decision-making, this study helps to bridge a substantial knowledge gap. Competing land-use systems are examined to secure central ecosystem services. To fulfill this obligation, an Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) serves as the modelling tool. InVEST assesses ecosystem services (ES) that are or may be affected by alternative bioenergy technologies. Spatially explicit model results include the water provisioning from the Water Yield Model (WYM), soil erosion and sedimentation described by the Sediment Delivery Ratio (SDR), and nutrient fluxes (N) in response to changing land use are obtained through the Nutrient Delivery Ratio (NDR). The detailed model results are finally extrapolated, which provides a comprehensive image of the environmental impacts associated with bioenergy expansion in Germany from our combination of unique Renewable Gas Plants (RGPs). The final result shows that nutrient load will reduce in southern Germany by the year 2050 compared to the reference state, and biomass use reduced by 46% crops. [ABSTRACT FROM AUTHOR]

Simple Summary: Global declines in arthropods necessitate a rethinking of current agricultural practices. Organic farming, complex landscapes with high proportions of seminatural habitats and local vineyard management practices such as implementation of flower-rich cover-crop mixtures may be a promising approach to enhance arthropod biodiversity and, thus, natural pest control in viticulture. We examined effects of organic farming, different types of vineyard inter-row vegetation, and landscape composition on natural pest control of a major grapevine pest, the grape berry moth Lobesia botrana, and identified its dominant natural enemies. Surprisingly, natural pest control was reduced by sown cover-crops and organic farming. Interestingly, bush crickets were one of the most dominant natural enemies in the Austrian study region. Negative effects of organic farming in our study are most likely related to high fungicide inputs. Thus, a promising approach to reduce fungicide input and, therefore, promote a more sustainable viticulture may be the implementation of fungus-resistant grape varieties. Habitat simplification and intensive use of pesticides are main drivers of global arthropod declines and are, thus, decreasing natural pest control. Organic farming, complex landscapes, and local vineyard management practices such as implementation of flower-rich cover-crop mixtures may be a promising approach to enhance predator abundance and, therefore, natural pest control. We examined the effect of organic versus integrated management, cover-crop diversity in the vineyard inter-rows, and landscape composition on the natural pest control of Lobesia botrana eggs and pupae. Predation of L. botrana pupae was reduced by organic farming and species-poor cover-crops by about 10%. Predation rates of L. botrana eggs did not differ significantly in any of the studied management options. Dominant predators were earwigs (Forficulidae), bush crickets (Tettigoniidae), and ants (Formicidae). Negative effects of organic viticulture are most likely related to the negative nontarget effects on arthropods related to the frequent sulfur and copper applications in combination with the avoidance of strongly damaging insecticides by integrated winegrowers. While a 10% difference in predation rates on a single pest stage is unlikely to have strong practical implications, our results show that the assumed effectiveness of environmentally friendly agriculture needs to be evaluated for specific crops and regions. [ABSTRACT FROM AUTHOR]

This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the validation tests. Even when reducing the data used for model training down to two or three mixture classes—which could be necessary or beneficial for the industrial application of our approach—sampling rates of n < 5 were sufficient in order to obtain significant predictions. [ABSTRACT FROM AUTHOR]