Numerical modeling of electromagnetic-based thermal recovery techniques combined with solvent injection

Sadeghi, Asghar; Harding, Thomas G.; MacFarlane, Bill; Bashti, Shima; Haghighat, Parnian

doi:10.1016/j.ijheatmasstransfer.2020.119393

Cited by 17 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the stochastic algorithms also handled problems arising in electromagnetics [39], astrophysics [40], electrical circuits [41], communication, signal processing, controls [42], plasma physics [43], bio-informatics [44], atomic physics [45], and nanotechnology [42]. These are inspirations for the author to investigate, analyze, and exploit research stochastic mathematical methodologies to establish a new, precise, robust, and dependable computing approach to investigate nonlinear reactive transport models that arise in soft tissue and microvessel studies.…”

Section: Introductionmentioning

confidence: 99%

Solution of Nonlinear Reaction-Diffusion Model in Porous Catalysts Arising in Micro-Vessel and Soft Tissue Using a Metaheuristic

et al. 2022

View full text Add to dashboard Cite

The steady-state reactive transport model (RTM) is a generalization of the nonlinear reaction-diffusion model in porous catalysts. The RTM is expressed as a non-linear ordinary differential equation of second-order with boundary conditions. Artificial neural network (ANN), Particle swarm optimization (PSO), and hybrid of PSO-SQP (Sequential Quadratic Programming) are used to obtain accurate, approximate solutions to the non-linear RTM. The proposed technique is applied to three different cases of non-linear RTM. The properties of the nonlinear reactive transport model in porous catalysts are investigated by considering various cases based on variation in the half-saturation concentration "α" and the characteristic reaction rate "β". The stability, reliability, and exactness of the proposed technique are established through comparison with the outcomes of the standard numerical procedure with the RK4 method and along with the different performance indices, which are Root-Mean-Square Error (RMSE), (TIC), Absolute Error (AE), and Mean Absolute Deviation (MAD). INDEX TERMS Artificial neural networks, nonlinear reactive transport model, particle swarm optimization, mathematical modeling.

show abstract

Section: Introductionmentioning

confidence: 99%