Probabilistic commodity price projections for unbiased techno-economic analyses
Probabilistic commodity price projections for unbiased techno-economic analyses
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DOI：
10.1016/j.engappai.2023.106065
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
S. Rodgers;Alexander Bowler;Fanran Meng;S. Poulston;J. McKechnie;A. Conradie
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文献类型：
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作者：
S. Rodgers;Alexander Bowler;Fanran Meng;S. Poulston;J. McKechnie;A. Conradie
技术经济分析是评估新技术和新工艺可行性的核心方法。分析的结果很大程度上取决于产品的价格，由从业者选择。具有代表性的未来价格分布需要作为投资的输入，敏感性，以及20至25年工厂寿命的不确定性分析。然而，目前的价格选择程序容易受到主观判断的影响，没有得到充分考虑，或者由于计算最低销售价格而被忽视。这项工作提出了一种机器学习方法来产生未来价格分布的无偏预测，用于技术经济分析。该方法使用100个具有长短期记忆层的神经网络模型的集合。这些模型是根据美国能源情报署（EIA）的长期原油预测和一种大宗商品的历史价格数据进行训练的。通过使用12年的历史数据预测未来26年五种商品化学品的价格来证明所提出的方法。除了从EIA预测中提取的经济前景外，五种商品的价格分布还捕获了每种商品特有的随机和确定性因素。当使用价格预测来修正前两次技术经济分析的净现值分布时，可以观察到统计学上显著的差异。这表明，在选择价格范围和分布时，依赖启发式方法并不能代表商品价格的不确定性。这项工作的新颖之处在于，它提出了一种无偏倚的机器学习方法，用于预测技术经济分析的长期概率价格，强调了不太严格的方法的缺陷。
Techno-economic analysis is a core methodology for assessing the feasibility of new technologies and processes. The outcome of an analysis is largely dictated by the product’s price, as selected by the practitioner. Representative future price distributions are required as inputs to investment, sensitivity, and uncertainty analyses across the 20 to 25 year plant life. However, current price selection procedures are open to subjective judgment, not adequately considered, or neglected by calculating a minimum selling price. This work presents a machine learning methodology to produce unbiased projections of future price distributions for use in a techno-economic analysis. The method uses an ensemble of 100 neural network models with Long Short-Term Memory layers. The models are trained on the Energy Information Administration’s (EIA) long-term crude oil projections and a commodity’s historic price data. The proposed method is demonstrated by projecting the price of five commodity chemicals 26 years into the future using 12 years of historic data. Alongside the economic outlook extracted from the EIA projections, the five commodity price distributions capture stochastic and deterministic elements specific to each commodity. A statistically significant difference was observed when using the price projections to revise the Net Present Value distributions for two previous techno-economic analyses. This suggests that relying on heuristics when selecting price ranges and distributions is unrepresentative of a commodity’s price uncertainty. The novelty of this work is the presentation of an unbiased machine learning approach to project long-term probabilistic prices for techno-economic analyses, emphasising the pitfalls of less rigorous approaches.