Mathematical model for integrated assessment of daily biogas plant operating modes using industrial production data

Source abstract

Information-analytical assessment of daily operating modes of an industrial biogas plant using production records from a supervisory control and data acquisition system. Aim. To improve the validity of integrated operating-efficiency assessment by accounting for feedstock-specific physical properties, the time inertia of anaerobic digestion, and dependence between criteria. Objectives. To correct the source-data structure and record counts; substantiate the criterion system; replace an equal fresh-mass feedstock coefficient with a substrate-specific methane-potential model; introduce a process lag; determine adaptive weights and operating-mode classes; perform physics-based and temporal consistency checks, analyze dependencies between criteria, conduct sensitivity analysis, and formulate operational recommendations. Methods. Daily production-log preprocessing, literature-based biochemical methane-potential coefficients and scenario TS and VS/TS values, a fixed 10-day lag, min-max normalisation, Shannon entropy, the CRITIC method, quartile classification, correlation analysis, and one-at-a-time weight sensitivity analysis were applied. Results. The 2024 dataset contains 366 daily records; the 10-day forward lag leaves 356 observations for the integrated performance score. The estimated methane potential averaged 3904.83 Nm3/day. Its correlation with future electricity generation was 0.176 at a 10-day lag, while the maximum within 0–30 days occurred at 13 days (0.227). Final weights for , , , , and were 0.3207, 0.1746, 0.0197, 0.0909, and 0.3941, respectively. Mean IPS was 0.4682; for active modes and . The 356 lag-valid days comprised 51 high-efficiency, 102 typical, 51 low-efficiency, and 152 shutdown modes. Perturbing one final weight by ±10% changed mean by at most 1.234%. Conclusions. The model provides a reproducible decomposable assessment of daily operation, avoids treating equal fresh masses of different substrates as energetically equivalent, explicitly accounts for process inertia, and supports diagnosis of the dominant causes of reduced performance. Transfer of the numerical weights and thresholds to other plants requires independent external validation.