Search

This study aimed to develop a novel, sustainable, highly efficient, and affordable salt processing technology tailored to local salt producers. Novel technology converts raw solar sea salt into the finest dried salt, which is widely accepted by various industries. Experiments were conducted at a drying temperature of 80°C, with an air velocity of 30 m/s, and a feed rate of 125 kg per hour to evaluate the proposed novel salt processing technology. Performance evaluation was based on product quality parameters (e.g. wet moisture content, sodium chloride (NaCl) percentage, and whiteness) and specific energy consumption. The economic assessment relied on financial analysis and payback period parameters. The results indicated a reduction in salt sample moisture content from 10.37% ± 0.4% to 0.47% ± 0.02%, with NaCl percentage and whiteness of the salt produced at 98.5% ± 0.3% and 81 ± 0.7, respectively. The specific energy consumption for the novel salt processing technology was 0.119 ± 0.07 kWh/kg of the final salt product. Initial investment requirements totalled IDR 275,435,000, with a payback period of 1.66 years. This proposed novel salt processing technology has the potential to generate high-quality dried salt with a production efficiency of 89.5%. Consequently, it could enhance local producer’s businesses, seize opportunities in the national salt market, support rural industrialisation, and be best configured for Indonesia’s local salt producers. [ABSTRACT FROM AUTHOR]

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies. [ABSTRACT FROM AUTHOR]

The overwhelming effects of climate change on the living environments has prompted several countries, including India into rolling out different carbon neutrality agenda. On this background, this study activated policy framework towards the attainment of India's 2070 net-zero emission target via energy efficiency. The roles of green-technology, affluence and population were also rectified. With quarterly series spanning 1997Q1–2021Q4 and estimates of the novel wavelet quantile correlation technique, the following insights sufficed. Energy-intensity generated significant carbon de-escalation effects mainly in the medium and long term. There were evidence of long-term asymmetric effects between them. Carbon intensity as well as green technology aggravated carbon emissions in both short and medium term, however, over the long term, they generated carbon neutrality effects. The empirical estimates also validated the environmental Kuznets curve (EKC) ideology given the long-term environmental quality enhancing attributes of GDP. India's population represents a major challenge for the net-zero emission target. But this can be curtailed through adequate orientations as prescribed in the LiFE progrmme. Among other considerations, India's net-zero target is realisable if the country extends its low-carbon energy profiles and deploy more green technologies. [ABSTRACT FROM AUTHOR]

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Qd, 1.0Qd, and 1.2Qd. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Qd, 1.0Qd, and 1.2Qd, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN). [ABSTRACT FROM AUTHOR]

Understanding the mechanisms behind memory, learning, and behavior is crucial to human development and significant research has been done in this area. Classical and operant conditioning and other theories of learning have elucidated different mechanisms of learning and how it modulates behavior. Even with advances in this area, questions remain on how to unlearn faulty ideas or extinguish maladaptive behaviors. In this paper, a novel theory to improve our understanding of this area is proposed. The theory proposes that as a consequence of the brain's energy efficiency evolutionary adaptations, all learning following memory consolidation, reconsolidation, and repeated reinforcements or strengthening over time, results in a phenomenon called mental representation block. The implications of this block on learning and behavior are significant and broad and include cognitive biases, belief in a creator or God, close-mindedness, dogmatism, physician misdiagnosis, racism, homophobia, and transphobia, susceptibility to deception and indoctrination, hate and love, artificial intelligence and creativity. [ABSTRACT FROM AUTHOR]