A comprehensive artificial neural network model for gasification process prediction
A comprehensive artificial neural network model for gasification process prediction
复制标题
DOI：
10.1016/j.apenergy.2022.119289
复制
发表时间：
2022-08
期刊：
影响因子：
11.2
通讯作者：
Simon Ascher;William Sloan;I. Watson;Siming You
中科院分区：
文献类型：
--
作者：
Simon Ascher;William Sloan;I. Watson;Siming You
竞争性生物质和废物气化方案的可行性和相对优点取决于相互作用因素的复杂组合。用于辅助决策的传统分析方法依赖于大量定义不明确的参数。在这里，我们开发了一种方法，通过使用机器学习、数据驱动的方法来预测气化技术性能的 10 个关键指标，从而避免过程表示中的不确定性。我们开发了一种人工神经网络，它在使用分类和连续数据输入方面是新颖的，这使得它灵活且广泛适用于评估气化工艺设计。它是第一个适用于各种原料类型、气化剂和反应器选项的模型。确定系数 (R2) 为 0.9310，证实了强大的预测性能。该方法有可能为成本效益分析（CBA）和生命周期可持续性评估（LCSA）等生成准确的输入数据，从而使政策制定者和投资者的决策更加透明。
The viability and the relative merits of competing biomass and waste gasification schemes depends on a complex mix of interacting factors. Conventional analytical methods that are used to aid decision making rely on a plethora of poorly defined parameters. Here we develop a method that eschews the uncertainty in process representation by using a machine learning, data driven, approach to predicting a set of 10 key measures of gasification technology’s performance. We develop an artificial neural network that is novel in its use of both categorical and continuous data inputs, which makes it flexible and broadly applicable in assessing gasification process designs. It is the first model applicable to a wide range of feedstock types, gasifying agents, and reactor options. A strong predictive performance, quantified by a coefficient of determination (R2) of 0.9310, was confirmed. The approach has the potential to generate accurate input data for e.g., cost-benefit analysis (CBA) and life cycle sustainability assessment (LCSA) and thus allow for more transparency in the decisions made by policy makers and investors.