Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT) of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST) in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO) were used as inputs and ST was used as output of the model.
Neural network prediction of parameters of biomass ashes, reused within the circular economy frame
Abstract Artificial neural networks were used for the prediction of three biomass ash fusion temperatures: initial deformation temperature IDT, hemispherical temperature HT and flow temperature FT based on chemical composition of the ash. Applicability of 400 neural network configurations (of linear, MLP, RBF and GRNN types) was verified statistically. Multilayer perceptron with 12 inputs representing fractions of the ash compounds, 11 hidden neurons and three outputs (IDT, HT, FT) proved to be the optimal neural model configuration. Statistical analysis suggested also, that considering intrinsic dispersion within the raw experimental data (literature data supplemented with the authors’ own results describing the halloysite addition effect), quality of the resulting 3-output IDT-HT-FT model (IDT prediction with R2 0.615, HT with R2 0.756 and FT with R2 0.729) could be regarded satisfactory for the identification and generalization of the discussed relationships. Analysis of the neural model sensitivity in respect to the input variables demonstrated, that the most important factors affecting all ash transition temperatures in the 3-output IDT-HT-FT model were: K2O, SiO2, CaO and Al2O3 fractions. Moreover, individual sensitivity in respect to IDT, HT and FT temperatures slightly varied (characteristics provided by independently established 1-output networks – IDT model, HT model and FT model, respectively). Statistically verified neural network working as the 3-output IDT-HT-FT model can be applied in various computational tasks in biofuels energy sector required by Industry 4.0 principles, as well as in the selected Circular Economy problems.
Sewage sludge applied to agricultural soil: Ecotoxicological effects on representative soil organisms.
Application of sewage sludge to agricultural lands is a current practice in EU. European legislation permits its use when concentrations of metals in soil do not increase above the maximum permissible limits. In order to assess the fate and the effects on representative soil organisms of sewage sludge amendments on agricultural lands, a soil microcosm (multi-species soil system-MS3) experiment was performed. The MS3 columns were filled with spiked soil at three different doses: 30, 60 and 120tha(-1) fresh wt. Seed plants (Triticum aestivum, Vicia sativa and Brassica rapa) and earthworms (Eisenia fetida) were introduced into the systems. After a 21-d exposure period, a statistically significant increase for Cd, Cu, Zn and Hg concentrations was found for the soils treated with the highest application rate. Dose-related increase was observed for nickel concentrations in leachates. Plants and earthworm metal body burden offer much more information than metal concentrations and help to understand the potential for metal accumulation. Bioaccumulation factor (BAF(plant-soil)) presented a different behavior among species and large differences for BAF(earthworm-soil), from control or sewage-amended soil, for Cd and Hg were found. B. rapa seed germination was reduced. Statistically significant decrease in fresh biomass was observed for T. aestivum and V. sativa at the highest application rate, whereas B. rapa biomass decreased at any application rate. Enzymatic activities (dehydrogenase and phosphatase) as well as respiration rate on soil microorganisms were enlarged.