A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

The different energy resource generation tends to have high-level variation, making the power supply complex for the end-users. Because of the intermittent nature, the variations occur by time, weather conditions, and output energy. Hence, this research aims to develop a new “Renewable Power Generation Prediction (RPGP)” model using Deep Learning (DL) to give the end user a reliable power supply. The data aggregation process initially accumulated the data in a normalized and structured format. Then, the data cleaning and scaling are performed to decrease the outliers and varying ranges of values. A higher-order statistical feature was attained from the cleaned and scaled data. This statistical feature was given to “Optimal Weight Computation Ensemble Dilated Deep Network (OWC-EDDNet)” to predict generated power. In this EDDLNet, networks such as “Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN)” are employed to predict the renewable generated power. Finally, the prediction score attained from all deep networks is multiplied by the optimized weight to get the final prediction outcome, where the weights are optimally determined with the support of the Enhanced Artificial Orcas Algorithm (EAOA). The extensive empirical results were analyzed among traditional algorithms and prediction models to showcase the efficacy of the designed energy generation prediction scheme

Imprints of lockdown and treatment processes on the wastewater surveillance of SARS-CoV-2 : a curious case of fourteen plants in Northern India

The present study investigated the detection of severe acute respiratory syndrome– coronavirus 2 (SARS-CoV-2) genomes at each treatment stage of 14 aerobic wastewater treatment plants (WWTPs) serving the major municipalities in two states of Rajasthan and Uttarakhand in Northern India. The untreated, primary, secondary and tertiary treated wastewater samples were collected over a time frame ranging from under-lockdown to post-lockdown conditions. The results showed that SARS-CoV-2 RNA was detected in 13 out of 40 wastewater samples in Jaipur district, Rajasthan and in 5 out of 14 wastewater samples in the Haridwar District, Uttarakhand with the E gene predominantly observed as compared to the N and RdRp target genes in later time-points of sampling. The Ct values of genes present in wastewater samples were correlated with the incidence of patient and community cases of COVID-19. This study further indicates that the viral RNA could be detected after the primary treatment but was not present in secondary or tertiary treated samples. This study implies that aerobic biological wastewater treatment systems such as moving bed biofilm reactor (MBBR) technology and sequencing batch reactor (SBR) are effective in virus removal from the wastewater. This work might present a new indication that there is little to no risk in relation to SARS-CoV-2 while reusing the treated wastewater for non-potable applications. In contrast, untreated wastewater might present a potential route of viral transmission through WWTPs to sanitation workers and the public. However, there is a need to investigate the survival and infection rates of SARS-CoV-2 in wastewater.Published versionThis research was funded by research grants from the Department of Biotechnology-GoI [Grant No. BT/RLF/Re-entry/12/2016]