دکتر محمد حجت

استادیار

گروه مهندسی شیمی
دانشکده فنی و مهندسی

پست الکترونیکی

m.hojjat@eng.ui.ac.ir

تلفن

+983137937074

آدرس

دانشگاه اصفهان میدان آزادی اصفهان، ایران
کد پستی : 8174673441

2009

2025

فعالیت های پژوهشی بر اساس سال

معرفی کلی

علاقه مندی پژوهشی

نانوسیالات جذب سطحی هوش محاسباتی

سابقه تحصیلی

دکتر, مهندسی شیمی, دانشگاه صنعتی اصفهان [ایران - اصفهان]
کارشناسی ارشد, مهندسی شیمی, دانشگاه صنعتی شریف [ایران - تهران]
کارشناسی, مهندسی شیمی, دانشگاه صنعتی شریف [ایران - تهران]

دروس

کارشناسی: موازنه جرم و انرژی - ترمودینامیک 1 - ترمودینامیک 2 - انتقال حرارت 1 - سینتیک و طراحی رآکتور - محاسبات عددی - ریاضیات مهندسی - فرآیند گاز کارشناسی ارشد: ترمودینامیک پیشرفته - ریاضیات مهندسی پیشرفته - عملیات فرآوری گاز - رئولوژی پیشرفته - روش تحقیق - خواص ترمودینامیکی سیالات دکترا: مدل سازی ریاضی فرآیندهای شیمیایی

برونداد‌های پژوهشی

همه موارد

مقالات

کتاب

2010

2022

مقالات لاتین

2022

Adsorption of Carbon Dioxide with Ni-MOF-74 and MWCNT Incorporated Poly Acrylonitrile Nanofibers

( Article )

Hojjat, M.

Publication Date: 2022

Nanomaterials (20794991)(3)

Numerical simulation and multi-objective optimization of heat transfer of Al2O3/water nanofluid in rectangular ducts

( Article )

Hojjat, M.

Publication Date: 2022

International Journal of Thermal Sciences (12900729)

Laminar flow of Al2O3/water nanofluids in rectangular ducts with different aspect ratios (AR) were simulated numerically. Both heat transfer coefficient and pressure drop of nanofluids are higher than the base fluid and increase with nanofluid concentration in ducts of all aspect ratios. For a duct with AR = 2, the heat transfer coefficient of nanofluids with a concentration of 5% vol. is on average 22% higher than the base fluid. Its pressure drop is about 2.5 times of the base fluids. The effect of nanofluid concentration on the heat transfer coefficient is more significant in ducts with a lower aspect ratio. The heat transfer coefficient of a 5% vol. nanofluid in a duct with AR = 1 is about 24% larger than the base fluid; this value is about 18% for a duct with AR = 6. The effect of nanofluid concentration on the pressure drop of all ducts is identical. The results of the numerical simulation were used for training a multi-Layer perceptron artificial neural network (MLP-ANN) model. The ANN model predicts the pressure drop and heat transfer coefficient of nanofluids in rectangular ducts very accurately. The maximum differences between the values of heat transfer coefficient predicted by the ANN and those obtained from the numerical simulation is about 0.11%, and that of the pressure drop is about 0.65%. Multi-objective NSGA-II optimization algorithm was applied to optimize the nanofluid flow in rectangular ducts. In optimization, the goal was to maximize the heat transfer coefficient and minimize the pressure drop. The values of objective functions were evaluated by the resulting ANN model. Optimization for ducts with each aspect ratio was performed. Optimization was also done for all channels. Pareto fronts were obtained from optimization. The best solution for each optimization is determined by two decision-making methods. For the overall optimal solution, nanofluid concentration, Reynolds number, and the duct aspect ratio are 2.28% vol., 400, and 6, respectively which are the same as the optimal solution of the duct with an aspect ratio of six. © 2021 Elsevier Masson SAS

2021

CO2Capture on an Adsorbent-Coated Finned Tube Heat Exchanger: Effect of the Adsorbent Thickness

( Article )

Hojjat, M.

Publication Date: 2021

Industrial and Engineering Chemistry Research (08885885)(12)

2020

Cooling performance of Newtonian and non-Newtonian nanofluids in a square channel: experimental investigation and ANN modeling

( Article )

Hojjat, M.

Publication Date: 2020

Journal Of Thermal Analysis And Calorimetry (13886150)(6)

Fabrication of SO42−/MO–Al2O3–ZrO2 (M = Ca, Mg, Sr, Ba) as Solid Acid–Base Nanocatalyst Used in Trans/Esterification Reaction

( Article )

Hojjat, M.

Publication Date: 2020

Waste and Biomass Valorization (18772641)(5)