| 1. |
- Hussain, Arif, et al.
(author)
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Methoxy-methylheptane as a cleaner fuel additive : An energy- and cost-efficient enhancement for separation and purification units
- 2021
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In: Energy Science & Engineering. - : John Wiley & Sons. - 2050-0505. ; :9, s. 1632-1646
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Journal article (peer-reviewed)abstract
- Environmental 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.
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| 2. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(author)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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In: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Journal article (peer-reviewed)abstract
- 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.
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| 3. |
- Ahmad, Iftikhar, et al.
(author)
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Drivers and Barriers for Efficient Energy Management Practices in Energy-Intensive Industries: A Case-Study of Iron and Steel Sector
- 2020
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In: Sustainability. - : MDPI. - 2071-1050. ; 12:18
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Journal article (peer-reviewed)abstract
- The two major reasons behind the worlds energy crisis are losses in energy transmission and less efficient energy use at sinks. The former flaw can be catered by changing the entire energy transmission system which requires investment and planning on a large scale, whereas the later deficiency can be overcome through proper management of energy utilizing systems. Energy-intensive industries have a substantial share in energy consumption and equally high energy saving potentials if they adopt some integrated and improved energy efficiency. This study investigates the energy management systems in the iron and steel sector of Pakistan, and compare it with findings of similar work in Sweden, Bangladesh, and Ghana. A systematic questionnaire was circulated in the iron and steel sector across the country and afterward the collected data was analyzed to find major barriers and drivers for efficient energy management practices. In addition, questions on non-energy benefits and information sources relevant to the energy efficiency were also part of the questionnaire. Cost reduction resulting from lowered energy use was rated as the most important driver for applying energy-efficient operation. On the other hand, the cost of production disruption was considered among high-level barriers to the implementation of improved energy efficiency. An increase in the life-time of equipment was labeled as the top non-energy benefits. Company peers and seminars/conferences were referred as the best information sources related to energy efficiency. The outcome of the study will be helpful to the decision-maker in the industry, as well as the government levels.
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