• Energy Research

AbstractEnvironmental protection agencies have begun imposing stringent regulations on the existing refineries to control the levels of gasoline additives. In this context, a novel compound, 2‐methoxy‐2‐methylheptane (MMH), had drawn attention as fuel additive for cleaner combustion. The conventional process of MMH production features three distillation columns in a direct sequence. These columns are used to maintain the required product purities and to utilize the unreacted reactants through recycling streams. The distillation system of the existing MMH plant can afford significant energy savings, leading to a reduction in the total annual costs (TAC). The aim of this investigation is to demonstrate that the reported conventional process can be significantly enhanced by modifying the design and operational parameters and by replacing two distillation columns with an intensified dividing wall column (DWC) configuration. The DWC design is further optimized using several algorithms such as the modified coordinate method (MCD), robust particle swarm paradigm (PSP), and firefly (FF) with nonlinear constraints. Compared to conventional process, the optimized DWC resulted in 24% and 11.5% savings in the plant operating and total annual costs, respectively.

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.

The depletion of conventional energy resources has drawn the world’s attention towards the use of alternate energy resources, which are not only efficient but sustainable as well. For this purpose, hydrogen is considered the fuel of the future. Liquid organic hydrogen carriers (LOHCs) have proved themselves as a potential option for the release and storage of hydrogen. The present study is aimed to analyze the performance of the perhydro-dibenzyl-toluene (PDBT) dehydrogenation system, for the release of hydrogen, under various operational conditions, i.e., temperature range of 270–320 °C, pressure range of 1–3 bar, and various platinum/palladium-based catalysts. For the operational system, the optimum operating conditions selected are 320 °C and 2 bar, and 2 wt. % Pt/Al2O3 as a suitable catalyst. The configuration is analyzed based on exergy analysis i.e., % exergy efficiency, and exergy destruction rate (kW), and two optimization strategies are developed using principles of process integration. Based on exergy analysis, strategy # 2, where the product’s heat is utilized to preheat the feed, and utilities consumption is minimized, is selected as the most suitable option for the dehydrogenation system. The process is simulated and optimized using Aspen HYSYS® V10.

The utilization of carbon dioxide to create valuable products such as methanol shows promise for addressing the issue of carbon emissions and global warming. Concurrently, it provides a solution to the intermittency and security of renewable energy supply via the water-splitting hydrogen production process. This power-to-methanol concept has gained increased attention because methanol is a liquid that can be conveniently stored and transported under ambient conditions. While direct air capture is an expensive solution, the carbon dioxide readily available from biogas can serve as a win-win situation. Similarly, water electrolysis technologies have modular, operational, and production challenges. In the present study, carbon dioxide was sourced from biogas via membrane separation, whereas H-2 was produced using plasma electrolysis. The entire power-to-methanol scenario was simulated using Aspen Plus v11. High purity and recovery of carbon dioxide and methane (99.51 mol.% and 98.29% and 98.88 mol.% and 99.68%, respectively) were achieved via membrane separation. The plasma reactor supplied H-2 with a mass yield of similar to 50%. Pure methanol (99.97%) was produced with a perpass conversion of 19.91% (15.7% higher than the base case). A detailed exergy analysis was performed on the process, highlighting the losses in heaters, separators, and reactors. Subsequent heat integration resulted in energy savings of 6.6%, while wind power as an energy source yielded carbon-neutral emissions. This conceptual study showcases the tremendous potential of the concept of zero-carbon-emission methanol production.

Abstract This study examined the thermodynamic effects of relative humidity (RH) on the performance of the natural gas liquefaction process. A single mixed refrigerant (SMR) liquefaction process was chosen for this study because of its simplicity and compactness. In addition, it is considered the most promising process for the liquefied natural gas (LNG) floating production, storage and offloading (FPSO) unit. The SMR process was optimized using a modified coordinate descent methodology, which resulted in 13.6% energy savings. Subsequently, an interface between commercial software Aspen Hysys® and MS-Excel VBA was carried out to study the effects of RH. The results showed that RH has pronounced effects on the performance of the LNG cycle by affecting the enthalpy balance around the air coolers, which ultimately affects the overall compression power, LNG exchanger performance, and other design and operational parameters. Furthermore, when the RH was increased from 0% to 95%, the UA value (product of overall heat transfer coefficient and heat transfer area) of the air coolers and the overall compression power decreased and increased linearly, respectively. Moreover, the heat transfer coefficient of the LNG cryogenic exchanger increased as a 4th order polynomial function in terms of the log-mean enthalpy difference. The results can provide insight into the selection of the appropriate design and operational parameters for the LNG plants associated with the regions of low or high relative humidity.

Abstract Liquefied natural gas (LNG) has attracted global attention as a more ecological energy source when compared to other fossil fuels. The nitrogen (N2) expander liquefaction is the most green and safe process among the different types of commercial natural gas liquefaction processes, but its relatively low energy efficiency is a major issue. To solve this issue, an energy-efficient, safe, and simple refrigeration cycle was proposed to improve the energy efficiency of the N2 based natural-gas liquefaction process. In the proposed refrigeration cycle, vortex tube as an expansion device was integrated with turbo-expander in order to reduce the overall required energy for LNG production. A well-known commercial simulator Aspen Hysys® v9 was employed for modeling and analysis of proposed LNG process. The hybrid vortex-tube turbo-expander LNG process resulted in the specific energy requirement of 0.5900 kWh/kg LNG. Furthermore, the energy efficiency of the proposed LNG process was also compared with previous N2 expander-based LNG processes. The results demonstrated that the proposed hybrid configuration saved up to 68.5% (depending on feed composition and conditions) in terms of the overall specific energy requirement in comparison with previous studies.

As an alternative to fossil fuels, biodiesel can be a source of clean and environmentally friendly energy source. However, its commercial application is limited by expensive feedstock and the slow nature of the pretreatment step-acid catalysis. The conventional approach to carry out this reaction uses stirred tank reactors. Recently, the lab-scale experiments using microbubble mediated mass transfer technology have demonstrated its potential use at commercial scale. However, all the studies conducted so far have been at a lab scale~100 mL of feedstock. To analyze the feasibility of microbubble technology, a larger pilot scale study is required. In this context, a kinetic study of microbubble technology at an intermediate scale is conducted (3 L of oil). Owing to the target for industrial application of the process, a commercial feedstock (Spirulina), microalgae oil (MO) and a commercial catalyst para-toluene sulfonic acid (PTSA) are used. Experiments to characterize the kinetics space (response surface, RSM) required for up-scaling are designed to develop a robust model. The model is compared with that developed by the gated recurrent unit (GRU) method. The maximum biodiesel conversion of 99.45 ± 1.3% is achieved by using these conditions: the molar ratio of MO to MeOH of 1:23.73 ratio, time of 60 min, and a catalyst loading of 3.3 wt% MO with an MO volume of 3 L. Furthermore, predicted models of RSM and GRU show proper fits to the experimental result. It was found that GRU produced a more accurate and robust model with correlation coefficient R2 = 0.9999 and root-mean-squared error (RSME) = 0.0515 in comparison with RSM model with R2 = 0.9844 and RMSE = 3.0832, respectively. Although RSM and GRU are fully empirical representations, they can be used for reactor up-scaling horizontally with microbubbles if the liquid layer height is held constant while the microbubble injection replicates along the floor of the reactor vessel—maintaining the tessellation pattern of the smaller vessel. This scaling approach maintains the local mixing profile, which is the major uncontrolled variable in conventional stirred tank reactor up-scaling.