Numerical model estimation of Biomethane production using an anaerobic CSTR: model formulation, parameter estimation and uncertainty/sensitivity analysis

There is an essential need to find an effective and reliable technique to transform organic waste into energy. The increasing knowledge of anaerobic processes as a reliable solution to transform organic waste into energy has led to the development and implementation of sophisticated and complex mathematical models. Quantitatively speaking, on 2017, the estimated generation of solid waste in the Sultanate of Oman amounts to about 1.7 million tonnes per annum (1.2 kg/capita/day). Those quantities are mostly directly dumped with no or little treatment. Despite the continuous efforts conducted by the local government, currently, there is still a significant volume of untreated solid waste material being sent to landfills, thus adding to the atmosphere, emissions of thousands of metric tonnes per annum of methane and carbon dioxide, the most adverse greenhouse gases. Therefore, deploying mechanisms to control organic waste dumping process is counted as one of the country priorities. Consequently, estimating in advance the biogas quantity of a given organic waste raw material become primordial for municipal solid waste managers. The latter can be done by using numerical models that have the ability to describe the anaerobic demanding processes. However, there is little consensus about the model’s structure and parametric identifiability questions. Those questions are not yet sufficiently elucidated in the reported anaerobic digestion modelling studies. In this paper, a complex numerical model is proposed for simulating the anaerobic biogas production from the co-digestion of organic waste material. An innovative complex dynamic model structure is proposed to support full-scale anaerobic plant design and operation decisions and to assist laboratory scale and pilot co-digestion research. The model facilitates the understanding of the co-digestion effects and therefore discards any potential negative impacts from mixing based on random or heuristic decisions. This paper introduces an innovative modelling procedure, including the application of uncertainty and global sensitivity analysis (LHS/PRCC/eFAST), which allows the study of a multi-dimensional parameter space globally so all uncertainties can be identified among the parameters; a multi-steps approach that gives a clear picture of the main sensitive model parameter. Among them, special concerns will be given to those identified as sensitive conducting to the digester failure. Sophisticated and stable algorithms are designed for the model cost function minimization criteria and result in an increase of the model accuracy. The model parameter uncertainty and sensitivity analysis revealed that the hydrolysis and acidogenesis phases are the most affecting steps of the methane production. A parameter such the polymer hydrolysis rate, the specific acidogens maximum growth rate, the saturation constant for acidogens, the specific acetoclastic methanogens maximum growth rate, the saturation constant for acetoclastic methanogens, and the gas-liquid mass transfer coefficient for CH4, contribute the most to the variance of the complex model estimate of methane.