Machine Learning is one of the fastest growing fields in academia. Many industries are aiming to incorporate machine learning tools into their day to day operation. However the keystone of doing so, is recognising when you have a problem which can be solved using machine learning. Adverse event prediction is one such problem. There are a wide range of methods for the production of sustainable energy. In many of which adverse events can occur which can impede energy production and even damage equipment. The two examples of adverse event prediction in sustainable energy production we examine in this paper are foam formation in anaerobic digestion and condenser fouling in steam turbines as used in nuclear power stations. In this paper we will propose a framework for: formalising a classification problem based around adverse event prediction, building predictive maintenance models capable of predicting these events before they occur and testing the reliability of these models.

中文翻译：

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使用机器学习预测低碳能源生产中的偶发不良事件

机器学习是学术界发展最快的领域之一。许多行业的目标是将机器学习工具融入日常运营中。然而，这样做的关键是认识到何时遇到可以使用机器学习解决的问题。不良事件预测就是这样一个问题。有多种方法可用于生产可持续能源。其中许多可能会发生不利事件，这会阻碍能源生产甚至损坏设备。我们在本文中研究的可持续能源生产中不良事件预测的两个例子是厌氧消化中的泡沫形成和核电站中使用的蒸汽轮机中的冷凝器结垢。在本文中，我们将提出一个框架，用于：将基于不良事件预测的分类问题形式化，