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Composting kinetics modeling is necessary to design and operate composting facilities that comply with strict market demands and tight environmental legislation. Current composting kinetics modeling can be characterized as inductive, i.e. the data are the starting point of the modeling process and determine the type of model used. It is argued that the inductive empirical approach has been developed to its limit of practicality. Further progress is not expected because of limits in measurement techniques and the resources needed to perform all experiments needed.Contrary to the inductive, the deductive modeling approach uses the existing theory as its starting point for model development. Deductive models of realistic situations contain many basic parameters representing the theoretical basis. These basic parameters however tend to be non-identifiable, limiting practical application.To overcome this problem, it is proposed that the basic parameters in the deductive model must be combined to a smaller number of so-called combined parameter that are identifiable. In this way a model is developed that can incorporate both the theoretical knowledge introduced via the basic parameter and the information of data as represented by the identifiable combined parameters.As an example of how information of both theory and data can be used, the case of the temperature effect on the composting rate is analyzed. The temperature effect is quantified as the activation energy E, a parameter derived from the well-known Arrhenius equation. The theoretical analysis shows that the E-value changes strongly during the process, which is very remarkable, as the E value of basic parameter remains constant. These results are in accordance with literature findings. The results suggest that the multiplicative approach used in first-order modeling should be reconsidered, as both the literature findings as well as the theoretical analysis of the model predict a shift in E-value. Missing a shift in the E-value could lead for instance to instability in temperature control algorithms.