Project Description:

Biogas plant operators often lack reliable tools to forecast gas output, resulting in inconsistent yields, inefficient operations, and poor feedstock planning. BioBoost AI addresses this gap by developing a cloud-based platform that uses machine learning to predict biogas yield from user-provided inputs. Anaerobic co-digesters play a vital role in renewable energy, but forecasting biogas production is challenging due to variable feedstocks and nonlinear microbial behavior. By combining predictive modeling with an accessible user interface, BioBoost AI empowers operators to make data-driven decisions that improve system performance, enhance methane yield, and support sustainable energy production.


Project Challenge:

Biogas plants face several persistent challenges that limit their efficiency and scalability. The most critical issue is the lack of real-time operational intelligence. Most small and medium-sized anaerobic digestion facilities operate manually, without access to predictive tools or sensor-based monitoring. As a result, plant operators struggle to maintain optimal conditions for gas production—leading to inconsistent biogas yields, inefficient feedstock utilization, and frequent system downtime. These inefficiencies increase operational costs and reduce the return on investment, making biogas a less attractive option for rural entrepreneurs and agri-businesses. Additionally, plant operators often lack the expertise to interpret data and make timely decisions, resulting in delayed responses to process faults or mechanical failures. Another major problem is the underutilization of digestate, the organic by-product, due to variability in quality and lack of standardization, which further limits revenue potential. These challenges hinder the widespread adoption of biogas as a clean energy solution and prevent plants from contributing effectively to circular economy and sustainability goals. BioBoost AI directly addresses these problems by providing a smart, AI-enabled platform that predicts gas output, detects anomalies early—enabling operators to optimize performance and make biogas production more efficient, reliable, and scalable.

Team Memebers:

Shen Gao

Suryanshu Pugla

Dorsa E.P. Moghaddam

Shiwen Yu

Keywords:

AutoML (TPOT), Biogas Prediction Modeling, Feature Engineering, AI, Cloud Computing Platform, Sustainable Energy