Research Topic

Applications of Monte Carlo Method in Chemical, Biochemical and Environmental Engineering

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About this Research Topic

Monte Carlo methods defined broadly a statistical approach to provide approximate solutions to mathematically complex optimization or simulation problems by using random sequences of numbers. The two main advantages of Monte Carlo methods are perhaps that the concept is relatively simple and easy to use and the same method has a sound basis: Law of large numbers ensures that the Monte Carlo solution converges asymptotically to the true solution of a problem. Since its first formulation by Metropolis and Ulam in 1949, Monte Carlo methods have seen widespread application across many scientific and engineering disciplines.

In this Research Topic, we welcome submissions on the applications of Monte Carlo methods within the domain of chemical, biochemical and environmental systems engineering. Original research articles as well as review papers are welcome covering fundamental and methodological research in developing Monte Carlo methods for uncertainty quantification, sensitivity analysis, sampling (stratified, space filling, Quasi Monte Carlo(QMC), Markov Chain Monte Carlo (MCMC), etc) to systems identification (parameter estimation in Bayesian inference), optimization, control and simulation problems. A special emphasis is given to understanding sources of uncertainties from measurements/data and how they propagate across different scales/components of the model (such as model parameters/coefficients, model structure/ assumptions and resolution) to final model outputs/predictions. Complementary to uncertainty quantification, sensitivity analysis contributions are welcomed for the assessment of model quality and the model fitness for its purpose. Under the context of uncertainty in data and model, we welcome systems engineering studies on supply chain and scheduling, process synthesis and design, process control and optimization under uncertainties within a broad range of topics including energy, water/wastewater, chemicals and life sciences (e.g. food, biotechnology and biopharmaceuticals).
This Research Topic aims to highlight two themes:

(A) The ground covered by Monte Carlo methods for better and effective use of engineering models in chemical and environmental systems engineering

(B) current challenges and limits with Monte Carlo methods and outline future development perspectives to push the frontiers in systems engineering further.


Keywords: Sampling, Monte Carlo, Modelling, Process-Product Design, Systems Analysis, Energy, Food, Water, Biopharmaceuticals


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Monte Carlo methods defined broadly a statistical approach to provide approximate solutions to mathematically complex optimization or simulation problems by using random sequences of numbers. The two main advantages of Monte Carlo methods are perhaps that the concept is relatively simple and easy to use and the same method has a sound basis: Law of large numbers ensures that the Monte Carlo solution converges asymptotically to the true solution of a problem. Since its first formulation by Metropolis and Ulam in 1949, Monte Carlo methods have seen widespread application across many scientific and engineering disciplines.

In this Research Topic, we welcome submissions on the applications of Monte Carlo methods within the domain of chemical, biochemical and environmental systems engineering. Original research articles as well as review papers are welcome covering fundamental and methodological research in developing Monte Carlo methods for uncertainty quantification, sensitivity analysis, sampling (stratified, space filling, Quasi Monte Carlo(QMC), Markov Chain Monte Carlo (MCMC), etc) to systems identification (parameter estimation in Bayesian inference), optimization, control and simulation problems. A special emphasis is given to understanding sources of uncertainties from measurements/data and how they propagate across different scales/components of the model (such as model parameters/coefficients, model structure/ assumptions and resolution) to final model outputs/predictions. Complementary to uncertainty quantification, sensitivity analysis contributions are welcomed for the assessment of model quality and the model fitness for its purpose. Under the context of uncertainty in data and model, we welcome systems engineering studies on supply chain and scheduling, process synthesis and design, process control and optimization under uncertainties within a broad range of topics including energy, water/wastewater, chemicals and life sciences (e.g. food, biotechnology and biopharmaceuticals).
This Research Topic aims to highlight two themes:

(A) The ground covered by Monte Carlo methods for better and effective use of engineering models in chemical and environmental systems engineering

(B) current challenges and limits with Monte Carlo methods and outline future development perspectives to push the frontiers in systems engineering further.


Keywords: Sampling, Monte Carlo, Modelling, Process-Product Design, Systems Analysis, Energy, Food, Water, Biopharmaceuticals


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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