Methods for anaerobic digestion model fitting—comparison between heuristic and automatic approach

Abstract

The article demonstrates if automatic optimization can be better than manual adjustment. The subject of optimization was the temperature-phased anaerobic digestion (TPAD) model. A selection of 3 parameters per each reactor in the process chain was appointed-reaction rate for propionate conversion, acetate conversion, and hydrolysis. Overall, both methods provided very convergent results. However, the total summary error (TSE) for the automatic algorithm was always moderately lower than for manual-the difference varied between 16.16 and 57.05 percentage points. Although the manual method has significant advantages-adjustment was more homogenous and gave more uniform fitting. Finally, cross-validation was performed to unify the values between the experimental series. The result was a total number of 4 values for each optimized constant-for two temperature points in each of two methods. Due to inconclusive information about the accuracy, averaged values were calculated to use in further researches. The recommendation from this article is to connect the best aspect of both methods to achieve the most accurate results. Keywords Biogas • TPAD • Model • Algorithm • Optimization Abbreviations δx The total error for iteration X θ Mean value of constant, from both series and methods f x The fractioning factor for a fraction x fun(k m;pro , k m;ac , k dis) The objective function in the optimization k dis Hydrolysis rate k m;x Uptake rate for substance X k x T Value of constant k x for the temperature T Q gas , Q gas exp Biogas flow for model or experiment Q in Input biomass flow to the reactor S x , S x exp The concentration of substance X, from model or experiment t sr The additional retention time for solids in addition to the hydraulic retention time V Rx The total volume of the reactor X X c Total feeding value of biomass

Innovations in anaerobic digestion: a model-based study

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Simulation of biogas synthesized via co-digestion processes

“…For parameter estimation, a sensitivity-based hierarchical as well as sequential individual parameter optimization technique was suggested, which is followed by a correlation-based approach. By using this technique, the total error and computation times are decreased, and fewer parameters need to be fitted to the data [73]. The accuracy and effectiveness of the calibration of the anaerobic digestion model are being improved by sophisticated parameter estimation methods such as particle swarm optimization-based smart algorithms [74].…”

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