Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant

Conversion of the toxic and hazardous Zanthoxylum armatum seed oil into methyl ester using green and recyclable silver oxide nanoparticles

The cleaner and sustainable production of biodiesel from toxic and hazardous non-edible seed oils offer a remarkable opportunity to deal with energy crises and provide a renewable substitute to depleting fossil fuels. In the current study, the potential of the novel, toxic and non-edible seed oil of Zanthoxylum armatum was investigated for eco-friendly production of biodiesel catalysed by green nanoparticles of silver oxide. Silver oxide nanoparticles were synthesised with aqueous leaf extract of Silybum marianum. Heterogeneous green nanocatalysts were preferred due to their recyclable nature and easy recovery. The maximum yield of 95% of methyl ester was obtained at optimum reaction conditions of oil to methanol molar ratio 1:7, catalyst loading 0.5 (wt.%), reaction temperature 90 °C and reaction time 2 h. Characterisation of synthesised nanoparticles of silver oxide was carried out with X-Ray diffraction (XRD), scanning electron microscopy (SEM), and energy diffraction X-ray (EDX). Fourier-transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) confirmed the formation of methyl esters. 5, 8-octadecenoic acid was found to be the major fatty acid methyl ester in the biodiesel sample. Fuel properties of biodiesel were investigated and found comparable to international standards of ASTM D-6571 and EN-14214. It was concluded from the current investigation that Zanthoxylum armatum is a potential biomass feedstock for the sustainable production of biodiesel using green nanoparticles of silver oxide