Department of Chemistry, Veer Surendra Sai University of Technology, Odisha, Burla, India.
Department of IT, Vardhaman College of Engineering, Hyderabad, Telangana, India.
Environ Sci Pollut Res Int. 2024 Jun;31(27):38893-38907. doi: 10.1007/s11356-023-26891-9. Epub 2023 Apr 20.
The current study focuses on the engine performance and emission analysis of a 4-stroke compression ignition engine powered by waste plastic oil (WPO) obtained by the catalytic pyrolysis of medical plastic wastes. This is followed by their optimization study and economic analysis. This study demonstrates the use of artificial neural networks (ANN) to forecast a multi-component fuel mixture, which is novel and reduces the amount of experimental effort required to determine the engine output characteristics. The engine tests were conducted using WPO blended diesel at various proportions (10%, 20%, 30% by volume) to acquire the required data for training the ANN model, which enables better prediction for the engine performance by making use of the standard back-propagation algorithm. Considering supervised data obtained from repeated engine tests, an artificial intelligence-based model of ANN was designed to select different parameters of performance and emission as output layers; at the same time, engine loading and different blending ratios of the test fuels were taken as the input layers. The ANN model was built up making use of 80% of testing outcomes for training. The ANN model forecasted engine performance and exhaust emission with regression coefficients (R) at 0.989-0.998 intervals and a mean relative error from 0.002 to 0.348%. Such results illustrated the effectiveness of the ANN model for estimating emissions and the performance of diesel engines. Moreover, the economic viability of the use of 20WPO as an alternative to diesel was justified by thermo-economic analysis.
本研究聚焦于通过医疗塑料废物的催化热解获得的废塑料油（WPO）为动力的四冲程压燃式发动机的引擎性能和排放分析。接着对其进行了优化研究和经济分析。本研究展示了使用人工神经网络（ANN）预测多组分燃料混合物的方法，这是新颖的，并且减少了确定发动机输出特性所需的实验工作量。使用 WPO 与柴油以不同比例（体积比为 10%、20%、30%）进行了发动机测试，以获取用于训练 ANN 模型的数据，该模型通过使用标准反向传播算法来更好地预测发动机性能。考虑到从重复的发动机测试中获得的监督数据，设计了基于人工智能的 ANN 模型，将不同的性能和排放参数选为输出层；同时，将发动机负载和不同的测试燃料混合比作为输入层。ANN 模型利用 80%的测试结果进行训练。ANN 模型预测发动机性能和排放的回归系数（R）在 0.989-0.998 之间，平均相对误差在 0.002 到 0.348%之间。这些结果表明了 ANN 模型在估计柴油机排放和性能方面的有效性。此外，通过热经济分析证明了使用 20WPO 替代柴油的经济可行性。
