A review on mathematical modeling of in-vessel composting process and energy balance

Ajmal, Muhammad; Shi, Aiping; Uddin, Saad; Awais, Muhammad; Faheem, Muhammad; Ye, Lihua; Rehman, Khalil Ur; Ullah, Muhammad Saif; Shi, Yefan

doi:10.1007/s13399-020-00883-y

Cited by 21 publications

(18 citation statements)

References 86 publications

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“…In addition to Mason [30], Li et al [10] reviewed the key variables affecting the food waste composting process and modeling approaches for describing the phenomena occurring during composting. In addition, Walling et al [2], Ajmal et al [8] and Yang et al [13] also reviewed the application of mathematical modeling in the composting process. Walling et al [2] discussed in detail the current limitations of the developed mathematical model and which aspects should be further researched and developed.…”

Section: Mathematical Modeling Of Composting Process 41 Some Basic Principles Of Composting Process Modelingmentioning

confidence: 99%

“…Walling et al [2] discussed in detail the current limitations of the developed mathematical model and which aspects should be further researched and developed. On the other hand, Ajmal et al [8] reviewed with special emphasis on the models describing the heat exchange during the process and Yang et al [13] with special emphasis on the changes of carbon, nitrogen, phosphorus and potassium during the composting process.…”

Section: Mathematical Modeling Of Composting Process 41 Some Basic Principles Of Composting Process Modelingmentioning

confidence: 99%

“…It is a biological process in which the polymeric waste materials contained in organic waste are degraded by the accelerated growth of fungi and bacteria [2,7]. In fact, it is a very complicated mechanism involving a variety of processes (microbiological, physicochemical and thermodynamic), all of which seem to be interrelated [8]. The products of the composting process are carbon dioxide and stable carbon forms that lead to the decomposition and mineralization of organic matter and the production of humic Processes 2022, 10, 229 2 of 22 substances [7].…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical models of the composting process assist getting information on how different process variables and conditions like substrate composition, oxygen concentration, pollutants concentrations, composting duration, temperature and etc. effect the compost quality during the process [8]. Furthermore, as noted by Mason [30], mathematical models can reduce or even replace the need for experimental work when investigating novel processes.…”

Section: Introductionmentioning

confidence: 99%

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Application of Optimization and Modeling for the Composting Process Enhancement

et al. 2022

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Composting is a more environmentally friendly and cost-effective alternative to digesting organic waste and turning it into organic fertilizer. It is a biological process in which polymeric waste materials contained in organic waste are biodegraded by fungi and bacteria. Temperature, pH, moisture content, C/N ratio, particle size, nutrient content and oxygen supply all have an impact on the efficiency of the composting process. To achieve optimal composting efficiency, all of these variables and their interactions must be considered. To this end, statistical optimization techniques and mathematical modeling approaches have been developed over the years. In this paper, an overview of optimization and mathematical modeling approaches in the field of composting processes is presented. The advantages and limitations of optimization and mathematical modeling for improving composting processes are also addressed.

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Section: Mathematical Modeling Of Composting Process 41 Some Basic Principles Of Composting Process Modelingmentioning

confidence: 99%

Section: Mathematical Modeling Of Composting Process 41 Some Basic Principles Of Composting Process Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Optimization and Modeling for the Composting Process Enhancement

et al. 2022

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“…The increase in parameters can lead to a rise in predictive uncertainty. 12 Some researchers believe that correction functions within the model are not necessary, but their neglect can lead to unreliable results, especially in the case of extreme temperature during thermophilic stage. It is also important to mention dynamic models in which n th order kinetics were used (Briški et al, 13 Barneto et al, 14 Papraćanin and Petric 8 ).…”

Section: Introductionmentioning

confidence: 99%

Kinetic Parameters Estimation and Model Evaluation for the Aerobic Bioconversion Process of the Organic Fraction of Municipal Solid Waste

Papraćanin¹

2022

Kemija U Industriji

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Aerobic bioconversion is very interesting process for the mathematical modelling and optimisation from the aspect of improving and accelerating the process, which is reflected in a higher organic matter decomposition rate. The aim of this research was to estimate the kinetic parameters in a dynamic model that described aerobic biodegradation of organic fraction of municipal solid waste (OFMSW) with various additions, from an engineering aspect. The presented mathematical model based on heat and mass transfer phenomena is a dynamic model for the gas-liquid-solid system. For the optimization and process simulation, three experiments were performed in three geometrically identical reactor systems at the same time, with different initial composition of composting mixtures. Data obtained experimentally from one reactor were used to optimize kinetic parameters of the model, and data from the other two reactors were used to verify and evaluate the dynamic model. Detailed statistical analysis showed narrow values of 95 % confidence interval for estimated parameters, indicating the appropriate accuracy of parameter estimation. Better data agreement was achieved in the reactor with a smaller ratio of OFMSW and additives, and the most accurately predicted dynamic state variable was the mass of organic matter. Parameter sensitivity analysis showed that the most sensitive parameters were reaction order n and activation energy E. Overall, the application of this model with optimised kinetic parameters of the aerobic biodegradation process represents a tool to predict the most important process variables (SD = 0.3378).

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