International Sugar Journal

Application of machine learning algorithms in boiler plant root cause analysis: a case study on an industrial scale biomass unit co-firing sugar cane bagasse and furfural residue at excessive final steam temperatures* [Full subscriber]

Abstract The current work sets out to showcase the power of statistical learning algorithms to mine boiler operational data in an attempt to create a predictive model capable of capturing the plant-specific behaviour. The machine learning predictive model can be used to perform investigations such as: boiler diagnostics, sensitivity analysis on operational parameters and root cause analysis to determine the cause of upset/detrimental conditions. A data mining analysis was performed on an industrial scale biomass boiler co-firing sugar cane bagasse and furfural residue, which operated at excessive final steam temperatures (420–440 °C) when compared with the design steam temperature (400…

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