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This study evaluates the pyrolysis of sewage sludge until 960 °C using heating rates between 3 K/min and 12 K/min in a macro TG/EGA. Mass and energy balances and kinetic parameters are determined. Thermal decomposition is divided into a low temperature zone (until 550 °C to 590 °C), for decomposition of organic matter, and a high temperature zone, for decomposition of inorganic matter and secondary reactions of the residual organic matter. In dry basis at 570 °C solid, liquid and gaseous products amount to 69.2 wt.-%, 29.2 wt.-% and 1.6 wt.-%, respectively. An increment in the final temperature to 960 °C causes a successive decrease of the solid residue to 56.9 wt.-%. The solid product contains more than 80 wt.-% mineral matter with high amounts of valuable elements, such as Ca and P. An energy requirement of 2.18 MJ/kg of dry sewage sludge is calculated for the pyrolysis until 570 °C. At this temperature, 58.5 % of the energy entering the process is concentrated in the liquid product and 40.0 % in the solid. A suitable set of kinetic parameters is determined through a formal independent parallel reactions model with six-pseudo components, using a combination of isoconversional and fitting methods.

This paper deals with new fractional models to follow the performance of dam water reverse osmosis (DWRO) desalination system using the dimensionless cumulative volume of alimentation, permeate and rejection. The experimental data consist of 2561 points taken during 4 years of the lifetime of 66 organics reverse osmosis (RO) membranes. The accuracy of the established fractional models was verified using statistical criteria and a comparison with ordinary models. The fractional dimensionless models (FDM) with optimal kinetic constants gave an accurate result and perfect consistency with the experimental data. As such, the coefficient of determination (R2) values were 0.9975, 0.9750 and 0.9801, with lower average absolute relative deviation (AARD) around 8.03, 0.53 and 0.45, through lower root mean squared error (RMSE) about 1.452, 0.976 and 0.880 for alimentation, permeate and rejection, respectively.

Dual-chamber microbial fuel cell (MFC) was constructed to treat heavy metal wastewater (Pb2+ solution). The results showed the maximum voltage, the maximum electric power density and pb2+ removal were 9.6 mV, 371.0 mV/m2 and 52.3% respectively. The effects of different cathode chamber environments on the production potential (V, volts) and Pb2+ removal rate of strained CD-1 were investigated. The results show that when the cathode chamber is soil, the maximum voltage is 8.88mV, and when the cathode chamber is solution, the maximum voltage is 12.11mV. The removal efficiency of heavy metals polluted from near to far from the electrode plate in the soil is 68.9%, 62.1%, 57.5%, and the removal efficiency of heavy metals in the solution is 67.46%. It can be concluded that when the soil is the cathode chamber. The removal rate of local heavy metals was the highest. When the solution was used as a cathode chamber, the average removal rate of heavy metals was the highest. The bacteria, named strain CD-1, as the electricity production bacteria (EPB) used in the dual chamber microbial fuel cell (MFC), was isolated from activate sludge and identified on its morphology, physichemical properties and phylogenetic positions. The results indicated that strain CD-1 was Gram-negative, rod. Oxidase and catalase reactions were positive. Strain CD-1 could use gluconate, lactose, D-fructose, L-Arabinose, D-arabinose, acetate, propionate, butyrate, lactate, sucrose and glucose, not use melibiose, galactose, N-acetylglucosamine, ethanol and glycerol.16S rRNA gene sequences analysis showed that strain CD-1 was most related to Ochrobactrum sp. (AJ245941）with the homology similarities of 98%. Based on the above results, strain CD-1 was belonged to the genus of Ochrobactrum and seemed to represent a novel species.

Recently, the environmental pollution is becoming a challenging issue due to the lacking of awareness about the importance of the environment and the growth of transportation facility and the various factories. The Internet of Things (IoT) technology is useful today for collecting and updating the data dynamically from anywhere anytime in less expense. Air quality of different cities is predicted by considering and maintaining the above listed technique which is useful for identifying and predicting the air quality and measure the air pollution level as well. This paper proposes a new IoT aware air quality prediction system that incorporates the newly proposed classifier with fuzzy temporal correlation and the auto-encoded Bidirectional Long Short-Term Memory (Bi-LSTM) to predict the diseases such as heart, cancer and diabetes. Moreover, we propose a new optimization algorithm called Spotted Hyena and Personalized best Cuckoo Search Optimization Algorithm (SHPCSA) for selecting and optimizing the most contributed features that are helpful for enhancing the prediction accuracy with short span of time. The air quality data is collected from various cities like Chennai, Bong Kong, etc. to conduct experiments. The proposed model is proved better than standard classifier with respect to precision, recall, F1-score and accuracy by conducting experiments