عنوان انگلیسی مقاله:
Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data
ترجمه فارسی عنوان مقاله:
نرم افزار طراحی شده توسط ماشین یادگیری برای پیش بینی دقیق تولید بیوگاز: یک مطالعه موردی در مورد داده های تولید چینی در مقیاس صنعتی
Sciencedirect - Elsevier - Journal of Cleaner Production, 218 (2019) 390-399: doi:10:1016/j:jclepro:2019:01:031
Djavan De Clercq a, b, 1, Devansh Jalota e, 1, Ruoxi Shang e, Kunyi Ni d, Zhuxin Zhang b, Areeb Khan b, c, Zongguo Wen a, *, Luis Caicedo f, Kai Yuan
The search for appropriate models for predictive analytics is currently a high priority to optimize
anaerobic fermentation processes in industrial-scale biogas facilities; operational productivity could be
enhanced if project operators used the latest tools in machine learning to inform decision-making. The
objective of this study is to enhance biogas production in industrial facilities by designing a graphical
user interface to machine learning models capable of predicting biogas output given a set of waste inputs.
The methodology involved applying predictive algorithms to daily production data from two major
Chinese biogas facilities in order to understand the most important inputs affecting biogas production.
The machine learning models used included logistic regression, support vector machine, random forest,
extreme gradient boosting, and k-nearest neighbors regression. The models were tuned and crossvalidated
for optimal accuracy. Our results showed that: (1) the KNN model had the highest model accuracy
for the Hainan biogas facility, with an 87% accuracy on the test set; (2) municipal fecal residue,
kitchen food waste, percolate, and chicken litter were inputs that maximized biogas production; (3) an
online web-tool based on the machine learning models was developed to enhance the analytical capabilities
of biogas project operators; (4) an online waste resource mapping tool was also developed for
macro-level project location planning. This research has wide implications for biogas project operators
seeking to enhance facility performance by incorporating machine learning into the analytical pipeline.
Keywords: Biogas | Machine learning | China | Graphical user interface