Areerat, Surat
Loading...
Preferred name
Areerat, Surat
Alternative Name
Areerat, S.
Main Affiliation
Email
surat.ar@kmitl.ac.th
3 results
Now showing 1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Solvent Selection for Mitragynine Extraction by Hansen Solubility Parameter(2023-01-01); Mitragyna speciosa (Korth.) Havil., commonly known as “Kratom” has a wide range of therapeutic benefits. In this research study the solvents that can be used to extract Mitragynine from kratom leaves. To predict solubility parameters, Hansen solubility parameters were employed. Then analyzing the solubility parameters using the ProCAPE application However, Mitragynine cannot directly determine the solubility. Instead, a method for evaluating the solubility from derivatives of Mitragynine was used in this study. A ternary graph shows the solubility of the derivatives and solvents. The solvents selected were considered for safety from the GSK’s Solvent Selection Guide. From the results, it was found that several types of solvents which that were dissolved derivatives of Mitragynine better than conventional solvents. Additionally, while using ProCAPE, the solubility parameters were predicted from extraction-related research was applied, and the results were shown as ternary graphs. The graphs were confirmed to be in accordance with the experimental data from the research that was collected. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Can an Appropriate CFD Model be Developed for Turbulent Flow in Rough Pipes?: Evidence from Friction Factor Prediction(2026-01-01); This paper answers the question: “How can an appropriate turbulent rough pipe flow computational fluid dynamics (CFD) model be developed?” The Reynolds-averaged Navier-Stokes equations with the standard k-epsilon turbulence model and scalable wall functions were solved to obtain Fanning friction factors and mean velocity profiles in inflectional and monotonic rough pipes. CFD models with near-wall grid sizes from four dimensionless wall distances and two roughness treatment approaches were simulated. Eight roughness Reynolds numbers, covering the lower end of the transitionally rough regime through the fully rough regime, were studied for each roughness type. Appropriate roughness and turbulence model constants for turbulent rough pipe flows in the transitionally rough regime were determined. For model validation, the predicted mean axial velocity profiles for Reynolds numbers of 5 × 10<sup>4</sup> and 5 × 10<sup>5</sup> exhibited good agreement with the reference experimental data. A total of 208 CFD simulations (32 from our previous works and 176 from the present study) were analyzed. Finally, based on comparisons between predicted Fanning friction factors and established correlations, appropriate CFD models for turbulent flows in inflectional and monotonic rough pipes were identified. Suitable CFD models for accurately predicting mean velocity profiles at roughness Reynolds numbers below 11.225 were also obtained, although with the caution that improved mean velocity prediction may reduce Fanning friction factor accuracy. Furthermore, the present CFD work provides essential guidance for extending simulations to other rough surface types and rough-wall flow situations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hybrid-AI-sep: A multi-agent computer-aided tool for separation process problems solving and learning(2026-06-01); Mansouri, Seyed SoheilAbstractThis paper presents a multi-agent based and artificial intelligence augmented computer-aided software tool, Hybrid-AI-sep, with problem solving and education modules for the important topic of design of separation operations. Three types of agents are employed by Hybrid-AI-sep, a library of knowledge-tools containing theory, concepts, and glossary terms related to separation operations; a library of database tools consisting of different types of measured data; and a library of computational tools that generate problem specific data that may be needed for decision making and explanation for the problem solution and educational modules. The paper presents software architecture together with examples of the three types of agents. Illustrative examples highlighting various features of Hybrid-AI-sep are given in the main manuscript and the supplementary material.