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Valorisation regarding bioethanol production residues by means of anaerobic digestion of food: Methane manufacturing and microbial residential areas.
Recent economic and environmental literature suggests that the current state of energy use in South Africa amidst rapid growing population is unsustainable. Researchers in this area mostly focus on the effect of fossil energy use on carbon (CO2) emission, which represents only an aspect of environmental quality. In contrast, the current study evaluates the influence of renewable energy use, human capital, and trade on ecological footprint--a more comprehensive measure of environmental quality. To this end, the study employs multiple structural breaks cointegration tests (Maki cointegration tests), dynamic unrestricted error correction model through Autoregressive Distributed Lag (ARDL) model, and VECM Granger causality tests. The results of the Maki cointegration tests reveal the existence of a cointegration between the variables in all the models with evidence of multiple structural breaks. Further, the ARDL results divulge that an increase in renewable energy use, human capital, and trade improves environmental quality through a decrease in ecological footprint, while an increase in income stimulates ecological footprint. Moreover, causal relationship is found, running from all the variables to renewable energy and trade flow in the long run, while in the short run, economic growth causes ecological footprint. Trade is found to Granger-cause human capital, while human capital causes renewable energy. Additionally, human capital, renewable energy, and economic growth are predictors of trade. The study therefore recommends South African policymakers to consider the importance of renewable energy, human capital development, and trade as a policy option to reduce ecological footprint and improve environmental quality.Oil sludge washing (OSW) with surfactants and co-solvents is used to recover the oil, and this process leaves some residuals (sediments and surfactant solution). Currently, there are no data on the ecotoxicological effects of these OSW residuals from different sludges. This study evaluated the toxicity of OSW residuals from washing four types of oil sludges with five surfactants (Triton X-100 and X-114, Tween 80, sodium dodecyl sulphate (SDS) and rhamnolipid) and a co-solvent (cyclohexane). The toxicity of the residuals was evaluated with the impact on the soil microbial dehydrogenase activity (DHA) and ryegrass (Lolium perenne) seed germination. There was a high DHA detected directly in the sludges and all OSW residual combinations, but this activity could not be attributed to the DHA itself but to some chemical interferences. The DHA was then tested in the soils amended with the OSW residuals to simulate a bioremediation scenario. There were no chemical interferences in this case. In general, the INTF conces if necessary.This paper presents a quantitative pollutant discharge model for a typical molybdenum roasting plant, which combines the best available technology and object-oriented Petri net concepts. The proposed model was used to verify whether the best available technology in a molybdenum roasting process meeting the current pollutant emission limits by comparing the results of multiple simulations with online monitoring data records. Theoretical SO2 emission values were obtained via multiple simulations and compared with the online monitoring data of a typical molybdenum roasting plant to verify the authenticity of the online monitoring data. The relationship between the different operating parameters and desulfurization efficiency is established through analyzing the historical operation parameters of the enterprise and response surface method. It was found that the optimal operating parameters for the flue gas desulfurization system of this plant could be characterized by a flue gas temperature of 90-93 °C, a pH range of 6.20-6.30, and a liquid-gas ratio of 23-25 L/m3.The filter cartridge dust collector has been widely used in industry, but the influence of its internal structure on its operation effects is rarely studied. FLUENT software was used to simulate the influence of different air volume and permeability values on the gas-solid two-phase flow of dust removal characteristics for a filter cartridge. The results show that when the air volume of the fan was greater than 1600 m3/h, the increase in the dust reduction rate was not obvious, and the high-velocity airflow filled the entire dust removal chamber, which was conducive to the filter using the largest effective filtration area to remove dust; the optimal air volume was 1600 m3/h. Furthermore, the dust removal effect gradually became worse when the porosity was higher than 0.65, but the fluidity of the internal air was poor when it was lower than 0.65. The optimum porosity was 0.65. A simulated validation analysis was conducted using the above optimal parameters. As the proportion of particles below 2 μm increased, the dust removal effect worsened.The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. PS-1145 mouse This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), were used to assess the performance of models. The data is also validated with the help of k-fold cross-validation using R2 and RMSE. The results indicated a strong correlation with NSE and R2 both above 0.85 for all the developed models. Gene expression programming (GEP) outperformed both artificial neural network (ANN) and linear and non-linear regression models for TDS and EC. The sensitivity and parametric analyses revealed that bicarbonate is the most sensitive parameter influencing both TDS and EC models. Two equations were derived and formulated to represent the novel results of GEP model to help authorities in the effective monitoring of river water quality.
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