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Dr. Mohammad Hosein Ahmadi
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

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0 Heat Transfer
0 Optimization
0 Thermodynamics
0 Cogeneration systems
0 Artificial intelligence methods

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Optimization
Heat Transfer
Thermodynamics
Artificial intelligence methods

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Journal article
Published: 06 August 2021 in Sustainability
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This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

ACS Style

Amir Zolghadri; Heydar Maddah; Mohammad Ahmadi; Mohsen Sharifpur. Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM). Sustainability 2021, 13, 8824 .

AMA Style

Amir Zolghadri, Heydar Maddah, Mohammad Ahmadi, Mohsen Sharifpur. Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM). Sustainability. 2021; 13 (16):8824.

Chicago/Turabian Style

Amir Zolghadri; Heydar Maddah; Mohammad Ahmadi; Mohsen Sharifpur. 2021. "Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)." Sustainability 13, no. 16: 8824.

Journal article
Published: 03 August 2021 in Sustainable Energy Technologies and Assessments
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The most of introduced schemes for the co-production of natural gas liquids (NGL) and liquefied natural gas (LNG) are designed as integrated configurations from the base. Furthermore, due to the high complexity of these structures single-objective optimizations are usually applied in this field. The present paper proposes a new NGL/LNG configuration with appropriate specifications based on an already built industrial NGL unit. The optimization approach in this study has the potential to advance current knowledge about multi-objective optimization assessment of integrated schemes. In this regard, the accurate surrogate models that describe the plant performance towards the annualized profit, specific power consumption, and exergy efficiency are determined by the response surface methodology. A genetic algorithm is utilized for the single-objective optimizations of these models, while their dual and triple-objective optimizations are performed with a controlled NSGA-II. To gain a robust decision, the best point from Pareto solutions in each of the multi-objective optimizations are selected by two well-known types of decision-making methods, where the criteria importance through the inter-criteria correlation (CRITIC) is applied for weighting the objectives. According to the results, the proposed scheme has higher economic, thermodynamic, and exergetic efficiencies among similar integrated configurations. The economic analysis reveals that the profit is increased by 669.99 $ min−1 with converting the existing NGL plant to the proposed integrated scheme. Meanwhile, the specific power consumption is set to its minimum value of 0.3473 kWh kg−1 LNG and exergy efficiency is reached to its maximum value of 55.12%. Moreover, the compressors located in the mixed refrigerant (MR) cycle have the largest share of the costs increasing. The highest power consumption and exergy destruction rates also belong to these equipments. The findings of this paper help for better understanding of the roles of annualized profit, specific power consumption, and exergy efficiency when they are simultaneously considered in the final optimal design of NGL/LNG co-production scheme. Furthermore, the purpose of this study will become even more apparent when there is no choice but to reduce the production costs for the survival of the LNG industry.

ACS Style

Omid Sabbagh; Mohammad Ali Fanaei; Alireza Arjomand; Mohammad Hossein Ahmadi. Multi-objective optimization assessment of a new integrated scheme for co-production of natural gas liquids and liquefied natural gas. Sustainable Energy Technologies and Assessments 2021, 47, 101493 .

AMA Style

Omid Sabbagh, Mohammad Ali Fanaei, Alireza Arjomand, Mohammad Hossein Ahmadi. Multi-objective optimization assessment of a new integrated scheme for co-production of natural gas liquids and liquefied natural gas. Sustainable Energy Technologies and Assessments. 2021; 47 ():101493.

Chicago/Turabian Style

Omid Sabbagh; Mohammad Ali Fanaei; Alireza Arjomand; Mohammad Hossein Ahmadi. 2021. "Multi-objective optimization assessment of a new integrated scheme for co-production of natural gas liquids and liquefied natural gas." Sustainable Energy Technologies and Assessments 47, no. : 101493.

Original article
Published: 15 July 2021 in Energy Science & Engineering
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This research is proposed to fully investigate the performance of a single-effect water/lithium bromide absorption chiller driven by geothermal energy. Since absorption cycles are considered as low-grade energy cycles, this innovative idea of rejecting fluid from a single-flash geothermal power plant with low-grade energy would serve as efficient, economical, and promising technology. In order to examine the feasibility of this approach, a residential building which is located in Sharjah, UAE, considered to evaluate its cooling capacity of 39 kW which is calculated using MATLAB software. Based on the obtained cooling load, modeling of the required water/lithium bromide single-effect absorption chiller machine is implemented and discussed. A detailed performance analysis of the proposed model under different conditions is performed using Engineering Equation Solver software (EES). Based on the obtained results, the major factors in the design of the proposed system are the size of the heat exchangers and the input heat source temperature. The results are presented graphically to find out the geofluid temperature and mass flow and solution heat exchanger effectiveness effects on the chiller thermal performance. Moreover, the effects of the size of all components of the absorption chiller on the cooling load to meet the space heating are presented. The thermal efficiency of the single-flash geothermal power plant is about 13% when the power plant is at production well temperature 250℃, separator pressure 0.24 MPa, and condenser pressure 7.5 kPa. The results show that the coefficient of performance (COP) reaches about 0.87 at solution heat exchanger effectiveness of 0.9, when the geofluid temperature is 120℃.

ACS Style

Mamdouh El Haj Assad; Milad Sadeghzadeh; Mohammad Hossein Ahmadi; Mohammad Al‐Shabi; Mona Albawab; Amjad Anvari‐Moghaddam; Ehab Bani Hani. Space cooling using geothermal single‐effect water/lithium bromide absorption chiller. Energy Science & Engineering 2021, 1 .

AMA Style

Mamdouh El Haj Assad, Milad Sadeghzadeh, Mohammad Hossein Ahmadi, Mohammad Al‐Shabi, Mona Albawab, Amjad Anvari‐Moghaddam, Ehab Bani Hani. Space cooling using geothermal single‐effect water/lithium bromide absorption chiller. Energy Science & Engineering. 2021; ():1.

Chicago/Turabian Style

Mamdouh El Haj Assad; Milad Sadeghzadeh; Mohammad Hossein Ahmadi; Mohammad Al‐Shabi; Mona Albawab; Amjad Anvari‐Moghaddam; Ehab Bani Hani. 2021. "Space cooling using geothermal single‐effect water/lithium bromide absorption chiller." Energy Science & Engineering , no. : 1.

Research article
Published: 15 July 2021 in International Journal of Energy Research
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Heat pipes are useful devices in heat transfer and particularly, in cooling systems. Given the high demand for cooling systems in various applications, an improvement in the performance of heat pipes has gained much attraction in recent years. In this study, the effects of utilizing working fluids with different thermal properties on the performance of pulsating heat pipes (PHP) are experimentally studied. Hence, nano-encapsulated phase change material (NPCM), reduced graphene oxide nanosheets, and their mixture, as a novel hybrid nanofluid, are prepared and dispersed in water as a working fluid. NPCM at 3 concentrations of 5, 10, and 20 g/L, as well as nanosheets at three concentrations of 0.3, 0.6, and 1.2 g/L, are synthesized and investigated. Moreover, both additives are mixed at five various concentrations to form a hybrid nanofluid. All experiments are conducted in the vertical orientation and filling ratio of 50%. It is found that due to the increase in viscosity with the increment of concentration, the highest concentration is not always the optimum concentration and the PHP's performance for each additive is optimized in a specific concentration. NPCMs could prevent the working fluid's temperature to rise by changing the phase, as a result, the effective specific heat of the working fluid is slightly augmented. Also, the thermal conductivity of the working fluid increased up to 13% using nanosheets. It is illustrated that using the mixture of nanosheets and NPCM leads to a 38% decline in thermal resistance. Meanwhile, it is found that the enhancement in the thermal performance of the PHP using nanofluids is not only purely due to the increase in thermal properties of nanofluids but also due to the increase in turbulence intensity and boiling nucleation sites created by the nanoparticles. Highlights Nano-encapsulated phase change material (PCM), reduced graphene oxide (RGO) nanosheets, and their mixture are used as working fluid in a pulsating heat pipe (PHP). The thermal performance of PHP is improved using PCM nanocapsules, RGO nanosheets, and their mixture. Nanosheets have a higher impact on the performance of PHP compared with nano-encapsulated PCM. The better performance of PHP using RGO nanosheets is due to the higher thermal conductivity and mixing of fluid containing them compared with pure water. The better performance of PHP using nanocapsules is attributed to the better mixing of the fluid.

ACS Style

Omid Mohammadi; Mohammad Behshad Shafii; Abbas Rezaee Shirin‐Abadi; Reza Heydarian; Mohammad Hossein Ahmadi. The impacts of utilizing nano‐encapsulated PCM along with RGO nanosheets in a pulsating heat pipe, a comparative study. International Journal of Energy Research 2021, 1 .

AMA Style

Omid Mohammadi, Mohammad Behshad Shafii, Abbas Rezaee Shirin‐Abadi, Reza Heydarian, Mohammad Hossein Ahmadi. The impacts of utilizing nano‐encapsulated PCM along with RGO nanosheets in a pulsating heat pipe, a comparative study. International Journal of Energy Research. 2021; ():1.

Chicago/Turabian Style

Omid Mohammadi; Mohammad Behshad Shafii; Abbas Rezaee Shirin‐Abadi; Reza Heydarian; Mohammad Hossein Ahmadi. 2021. "The impacts of utilizing nano‐encapsulated PCM along with RGO nanosheets in a pulsating heat pipe, a comparative study." International Journal of Energy Research , no. : 1.

Article
Published: 10 July 2021 in Journal of Thermal Analysis and Calorimetry
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ACS Style

Mohammad Hossein Ahmadi; Emin Açıkkalp. Exergetic dimensions of energy systems and processes. Journal of Thermal Analysis and Calorimetry 2021, 145, 631 -634.

AMA Style

Mohammad Hossein Ahmadi, Emin Açıkkalp. Exergetic dimensions of energy systems and processes. Journal of Thermal Analysis and Calorimetry. 2021; 145 (3):631-634.

Chicago/Turabian Style

Mohammad Hossein Ahmadi; Emin Açıkkalp. 2021. "Exergetic dimensions of energy systems and processes." Journal of Thermal Analysis and Calorimetry 145, no. 3: 631-634.

Review
Published: 29 May 2021 in The European Physical Journal Plus
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The majority of existing water is saline water and it is crucial to find approaches and technologies to desalinate water in an efficient and reliable manner. Solar energy can be applied in desalination systems in order to provide required heat or generate needed electricity by using PV modules. Applying solar energy instead of fossil fuels leads to more environmentally benign technologies in desalinating saline water. Due to the severe worldwide water crisis, precise comprehension of desalination methods can pave the way toward potable water achievement at reasonable cost. In this paper, a comprehensive literature review is accomplished on various types of desalination systems and applications of solar energy in these technologies. Based on the reviewed studies, solar energy is a preferable source of energy for fresh water production with lower greenhouse gases emission and high operation reliability.

ACS Style

S. Mohsen Pourkiaei; Mohammad Hossein Ahmadi; Mahyar Ghazvini; Soroush Moosavi; Fathollah Pourfayaz; Ravinder Kumar; Lingen Chen. Status of direct and indirect solar desalination methods: comprehensive review. The European Physical Journal Plus 2021, 136, 1 -36.

AMA Style

S. Mohsen Pourkiaei, Mohammad Hossein Ahmadi, Mahyar Ghazvini, Soroush Moosavi, Fathollah Pourfayaz, Ravinder Kumar, Lingen Chen. Status of direct and indirect solar desalination methods: comprehensive review. The European Physical Journal Plus. 2021; 136 (5):1-36.

Chicago/Turabian Style

S. Mohsen Pourkiaei; Mohammad Hossein Ahmadi; Mahyar Ghazvini; Soroush Moosavi; Fathollah Pourfayaz; Ravinder Kumar; Lingen Chen. 2021. "Status of direct and indirect solar desalination methods: comprehensive review." The European Physical Journal Plus 136, no. 5: 1-36.

Journal article
Published: 09 April 2021 in Energy Reports
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Owing to enormous increase in the automobile population, traditional petroleum fuels for internal combustion engines will be usable only for a few years. Moreover, by releasing toxic emissions into the atmosphere at higher levels these fuels create severe environmental problems. In general, Carbon-monoxide (CO), Unburnt hydrocarbons (UHC), and smoke are emissions emitted by engines. The pollution of the environment can be controlled by replacing petroleum fuel with the use of alternative fuels like acetylene gas, hydrogen, CNG, LPG etc. In this current research, an experimental investigation was carried out in Compression Ignition engine using acetylene fuel, further enhancing the performance and emission properties by using diethyl ether (DEE) and ethanol as oxygenated fuel. In view of the performance and outflow (emission) parameters optimizing valve of acetylene gas as 12 lpm with oxygenated fuels, the performance and outflow characteristics were improved. The findings show an enhancement in brake thermal-efficiency of up to 3 to 4%, a decrease in exhaust temperature and emissions like CO, UHC and smoke of up to 40%, 20%–30% and 10%–35% respectively, there is decrease in fuel consumption of 10%–30%. It was found that ethanol is better than diethyl ether and diesel fuels when used as an oxygenated fuel with acetylene gas. In view of the performance and emission parameters, the best oxygenated fuel is ethanol and the optimize blend for diesel engine is E15+A12.

ACS Style

Gursharan Singh; Shubham Sharma; Jujhar Singh; Som Kumar; Yadvinder Singh; Mohammad H. Ahmadi; Alibek Issakhov. Optimization of performance, combustion and emission characteristics of acetylene aspirated diesel engine with oxygenated fuels: An Experimental approach. Energy Reports 2021, 7, 1857 -1874.

AMA Style

Gursharan Singh, Shubham Sharma, Jujhar Singh, Som Kumar, Yadvinder Singh, Mohammad H. Ahmadi, Alibek Issakhov. Optimization of performance, combustion and emission characteristics of acetylene aspirated diesel engine with oxygenated fuels: An Experimental approach. Energy Reports. 2021; 7 ():1857-1874.

Chicago/Turabian Style

Gursharan Singh; Shubham Sharma; Jujhar Singh; Som Kumar; Yadvinder Singh; Mohammad H. Ahmadi; Alibek Issakhov. 2021. "Optimization of performance, combustion and emission characteristics of acetylene aspirated diesel engine with oxygenated fuels: An Experimental approach." Energy Reports 7, no. : 1857-1874.

Review
Published: 15 February 2021 in Journal of Thermal Analysis and Calorimetry
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Accurate modeling of heat pipes, as a two-phase thermal medium, is the main goal of several research works. Since the effective thermal conductivity of the heat pipes is dependent on different factors, the modeling procedure is complicated. The main techniques presented for modeling the heat pipes are numerical methods, i.e., computational fluid dynamics, and machine learning approaches. Due to the simplicity of the use of machine learning methods, these types of models can be more applicable and interesting for scientists compared with numerical models. In this regard, different types of intelligence methods, including Support Vector Machine and Artificial Neural Network, have been employed for determining and estimating the thermal behavior of various kinds of heat pipes. The precision and applicability of these intelligence models depend on different items, such as the input variable, used algorithm, and structure of the model. In the present work, recent studies performed on the applications of intelligence models in the modeling of heat pipes are reviewed, and their primary outcomes are represented. Based on the findings of the studies, intelligence models can estimate the effective heat transfer coefficients of heat pipes reliably. Also, it is concluded that applying the optimization approach in the structure of models, for minimizing the deviation of the predicted values from the actual ones, results in the accuracy enhancement of the models. Finally, some suggestions are provided for future researches concerning the modeling of heat pipes by employing data-driven approaches.

ACS Style

Mohammad Hossein Ahmadi; Ravinder Kumar; Mamdouh El Haj Assad; Phuong Thao Thi Ngo. Applications of machine learning methods in modeling various types of heat pipes: a review. Journal of Thermal Analysis and Calorimetry 2021, 1 -9.

AMA Style

Mohammad Hossein Ahmadi, Ravinder Kumar, Mamdouh El Haj Assad, Phuong Thao Thi Ngo. Applications of machine learning methods in modeling various types of heat pipes: a review. Journal of Thermal Analysis and Calorimetry. 2021; ():1-9.

Chicago/Turabian Style

Mohammad Hossein Ahmadi; Ravinder Kumar; Mamdouh El Haj Assad; Phuong Thao Thi Ngo. 2021. "Applications of machine learning methods in modeling various types of heat pipes: a review." Journal of Thermal Analysis and Calorimetry , no. : 1-9.

Article
Published: 05 February 2021 in Journal of Thermal Analysis and Calorimetry
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Zero liquid discharge (ZLD) has shown to be a promising technology to recycle water with good quality. The ZLD objective is to purify the water from all the liquid waste. The ZLD approach is concentrated on reducing wastewater for possible reuse. In conventional ZLD systems, thermal processes are fundamental. The biggest challenge to implement thermal ZLD systems widely is its intensive energy consumption. As a solution, thermal ZLD systems are integrated with membrane-based reverse osmosis (RO) technology to reduce both capital and operational costs. This study, therefore, focuses on the optimizing a RO/thermal ZLD system based on one of the most important parameters of design—the salinity of the reject brine of evaporator. To give more practical aspect to the results, solution is based realistic design data of a petrochemical complex as the producer of ammonia (2050 ton day−1) and urea (3250 ton day−1). Results show that increasing the salinity of brine stream in evaporator reduces the total required heating surface area of the ZLD plant as well as its required power. This decrease is evident at lower amounts of Xb, but the rate is lowered with increasing of this parameter. So, further increase in Xb does not have much effect on reducing the total heating surface area and power consumption. It means that there is an optimum amount of Xb which can be selected for different applications.

ACS Style

Sharare Mohammadi; Mohammad Hossein Ahmadi; Ramin Ehsani. Optimization of combined Reverse Osmosis: thermal Zero Liquid Discharge system parameters for an Ammonia and Urea production complex. Journal of Thermal Analysis and Calorimetry 2021, 144, 1863 -1871.

AMA Style

Sharare Mohammadi, Mohammad Hossein Ahmadi, Ramin Ehsani. Optimization of combined Reverse Osmosis: thermal Zero Liquid Discharge system parameters for an Ammonia and Urea production complex. Journal of Thermal Analysis and Calorimetry. 2021; 144 (5):1863-1871.

Chicago/Turabian Style

Sharare Mohammadi; Mohammad Hossein Ahmadi; Ramin Ehsani. 2021. "Optimization of combined Reverse Osmosis: thermal Zero Liquid Discharge system parameters for an Ammonia and Urea production complex." Journal of Thermal Analysis and Calorimetry 144, no. 5: 1863-1871.

Journal article
Published: 28 January 2021 in Journal of Environmental Management
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During the past three decades, harmful algal blooms (HAB) events have been frequently observed in marine waters around many coastal cities in the world including Hong Kong. The increasing occurrence of HAB has caused acute influences and damages on water environment and marine aquaculture with millions of monetary losses. For example, the Tolo Harbour is one of the most affected areas in Hong Kong, where more than 30% HAB occurred. In order to forewarn the potential HAB incidents, the machine learning (ML) methods have been increasingly resorted in modelling and forecasting water quality issues. In this study, two different ML methods – artificial neural networks (ANN) and support vector machine (SVM) – are implemented and improved by introducing different hybrid learning algorithms for the simulations and comparative analysis of more than 30-year measured data, so as to accurately forecast algal growth and eutrophication in Tolo Harbour in Hong Kong. The application results show the good applicability and accuracy of these two ML methods for the predictions of both trend and magnitude of the algal growth. Specifically, the results reveal that ANN is preferable to achieve satisfactory results with quick response, while the SVM is suitable to accurately identify the optimal model but taking longer training time. Moreover, it is demonstrated that the used ML methods could ensure robustness to learn complicated relationship between algal dynamics and different coastal environmental variables and thereby to identify significant variables accurately. The results analysis and discussion of this study also indicate the potentials and advantages of the applied ML models to provide useful information and implications for understanding the mechanism and process of HAB outbreak and evolution that is helpful to improving the water quality prediction for coastal hydro-environment management.

ACS Style

Tianan Deng; Kwok-Wing Chau; Huan-Feng Duan. Machine learning based marine water quality prediction for coastal hydro-environment management. Journal of Environmental Management 2021, 284, 112051 .

AMA Style

Tianan Deng, Kwok-Wing Chau, Huan-Feng Duan. Machine learning based marine water quality prediction for coastal hydro-environment management. Journal of Environmental Management. 2021; 284 ():112051.

Chicago/Turabian Style

Tianan Deng; Kwok-Wing Chau; Huan-Feng Duan. 2021. "Machine learning based marine water quality prediction for coastal hydro-environment management." Journal of Environmental Management 284, no. : 112051.

Original article
Published: 19 January 2021 in Engineering with Computers
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Yin–Yang-pair Optimization (YYPO) is a recently developed philosophy-inspired meta-heuristic algorithm, which works with two main points for exploitation and exploration, respectively, and then generates more points via splitting to search the global optimum. However, it suffers from low quality of candidate solutions in its exploration process owing to the lack of elitism. Inspired by this, a new modified algorithm named orthogonal opposition-based-learning Yin–Yang-pair Optimization (OOYO) is proposed to enhance the performance of YYPO. First, the OOYO retains the normalization operation in YYPO and starts with a single point to exploit. A set of opposite points is designed by a method of opposition-based learning with split points generated from the current optimum for exploration. Then, the points, i.e., candidate solutions, are constructed by the randomly selected split point and opposite points through the idea of orthogonal experiment design to make full use of information from the space. The proposed OOYO does not add additional time complexity and eliminates a user-defined parameter in YYPO, which facilitates parameter adjustment. The novel orthogonal opposition-based learning strategy can provide inspirations for the improvement of other optimization algorithms. Extensive test functions containing a classic test suite of 23 standard benchmark functions and 2 test suites of Swarm Intelligence Symposium 2005 and Congress on Evolutionary Computation 2020 from Institute of Electrical and Electronics Engineers are employed to evaluate the proposed algorithm. Non-parametric statistical results demonstrate that OOYO outperforms YYPO and furnishes strong competitiveness compared with other state-of-the-art algorithms. In addition, we apply OOYO to solve four well-known constrained engineering problems and a practical problem of parameters optimization in a rainstorm intensity model.

ACS Style

Wen-Chuan Wang; Lei Xu; Kwok-Wing Chau; Yong Zhao; Dong-Mei Xu. An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization. Engineering with Computers 2021, 1 -35.

AMA Style

Wen-Chuan Wang, Lei Xu, Kwok-Wing Chau, Yong Zhao, Dong-Mei Xu. An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization. Engineering with Computers. 2021; ():1-35.

Chicago/Turabian Style

Wen-Chuan Wang; Lei Xu; Kwok-Wing Chau; Yong Zhao; Dong-Mei Xu. 2021. "An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization." Engineering with Computers , no. : 1-35.

Journal article
Published: 08 January 2021 in Nanomaterials
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The superiority of nanofluid over conventional working fluid has been well researched and proven. Newest on the horizon is the hybrid nanofluid currently being examined due to its improved thermal properties. This paper examined the viscosity and electrical conductivity of deionized water (DIW)-based multiwalled carbon nanotube (MWCNT)-Fe2O3 (20:80) nanofluids at temperatures and volume concentrations ranging from 15 °C to 55 °C and 0.1–1.5%, respectively. The morphology of the suspended hybrid nanofluids was characterized using a transmission electron microscope, and the stability was monitored using visual inspection, UV–visible, and viscosity-checking techniques. With the aid of a viscometer and electrical conductivity meter, the viscosity and electrical conductivity of the hybrid nanofluids were determined, respectively. The MWCNT-Fe2O3/DIW nanofluids were found to be stable and well suspended. Both the electrical conductivity and viscosity of the hybrid nanofluids were augmented with respect to increasing volume concentration. In contrast, the temperature rise was noticed to diminish the viscosity of the nanofluids, but it enhanced electrical conductivity. Maximum increments of 35.7% and 1676.4% were obtained for the viscosity and electrical conductivity of the hybrid nanofluids, respectively, when compared with the base fluid. The obtained results were observed to agree with previous studies in the literature. After fitting the obtained experimental data, high accuracy was achieved with the formulated correlations for estimating the electrical conductivity and viscosity. The examined hybrid nanofluid was noticed to possess a lesser viscosity in comparison with the mono-particle nanofluid of Fe2O3/water, which was good for engineering applications as the pumping power would be reduced.

ACS Style

Solomon O. Giwa; Mohsen Sharifpur; Mohammad H. Ahmadi; S. M. Sohel Murshed; Josua P. Meyer. Experimental Investigation on Stability, Viscosity, and Electrical Conductivity of Water-Based Hybrid Nanofluid of MWCNT-Fe2O3. Nanomaterials 2021, 11, 136 .

AMA Style

Solomon O. Giwa, Mohsen Sharifpur, Mohammad H. Ahmadi, S. M. Sohel Murshed, Josua P. Meyer. Experimental Investigation on Stability, Viscosity, and Electrical Conductivity of Water-Based Hybrid Nanofluid of MWCNT-Fe2O3. Nanomaterials. 2021; 11 (1):136.

Chicago/Turabian Style

Solomon O. Giwa; Mohsen Sharifpur; Mohammad H. Ahmadi; S. M. Sohel Murshed; Josua P. Meyer. 2021. "Experimental Investigation on Stability, Viscosity, and Electrical Conductivity of Water-Based Hybrid Nanofluid of MWCNT-Fe2O3." Nanomaterials 11, no. 1: 136.

Journal article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

ACS Style

Shahab S. Band; Essam Heggy; Sayed M. Bateni; Hojat Karami; Mobina Rabiee; Saeed Samadianfard; Kwok-Wing Chau; Amir Mosavi. Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1147 -1158.

AMA Style

Shahab S. Band, Essam Heggy, Sayed M. Bateni, Hojat Karami, Mobina Rabiee, Saeed Samadianfard, Kwok-Wing Chau, Amir Mosavi. Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1147-1158.

Chicago/Turabian Style

Shahab S. Band; Essam Heggy; Sayed M. Bateni; Hojat Karami; Mobina Rabiee; Saeed Samadianfard; Kwok-Wing Chau; Amir Mosavi. 2021. "Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1147-1158.

Review
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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In biodiesel production by trans-esterification, one of the essential compound is glycerin. Global glycerin production is increasing significantly, projecting a global value reduction for glycerol. Consequently the scientific community had been encouraged to investigate converting glycerol into more valuable products. In this research, the primary sources and processes of biodiesel production are surveyed. Where the processes that involve glycerin are reviewed and the diesel engine performance and emissions under variant states are discussed. According to the results of this study, it is reported that the choice of an optimal diesel/biodiesel significantly depends on the materials, additives and the engine condition. Glycerol etherification, carboxylation, and glycerol carbonate, however, had been identified as the widely manufactured and used additives. It is further observed that the use of these such additives has reduced several emissions, which is an important factor. In addition, it is suggested that using glycerin additives improves the properties of biodiesel. Acetone, on the other hand is introduced as one of the most important additives in the combination of diesel and biodiesel fuel due to the reduction of maximum emission. The presence of hydroxyl groups can reduce NOx. Finally, the diethyl ether additive can be mentioned which increases the thermal efficiency and increases the brake-specific fuel consumption (BSFC).

ACS Style

Farid Haghighat Shoar; Bahman Najafi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1191 -1228.

AMA Style

Farid Haghighat Shoar, Bahman Najafi, Shahab S. Band, Kwok-Wing Chau, Amir Mosavi. Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1191-1228.

Chicago/Turabian Style

Farid Haghighat Shoar; Bahman Najafi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. 2021. "Different scenarios of glycerin conversion to combustible products and their effects on compression ignition engine as fuel additive: a review." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1191-1228.

Journal article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.

ACS Style

Linyuan Fan; Maryam Abbasi; Kazhal Salehi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1159 -1175.

AMA Style

Linyuan Fan, Maryam Abbasi, Kazhal Salehi, Shahab S. Band, Kwok-Wing Chau, Amir Mosavi. Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1159-1175.

Chicago/Turabian Style

Linyuan Fan; Maryam Abbasi; Kazhal Salehi; Shahab S. Band; Kwok-Wing Chau; Amir Mosavi. 2021. "Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1159-1175.

Short communication
Published: 28 December 2020 in Case Studies in Thermal Engineering
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Nucleate pool steaming is an effective mode of transfer of heat that helps to reduce the use of fossil fuels and thus reduce pollution. Transfer of heat in nucleate pool steaming is examined to occur through the combination of natural convection, enhanced latent heat and convection transport. At the intermediate heat flux range, all three components play a principal character. In the elevated flux of heat area as the heat flux increases the enhanced convection contribution decreases while latent heat transport contribution has been found to increase considerably. In this study, we attempt to develop a heat transfer relationship for the coefficient of transfer of heat and suggested based on the relative benefactions of three components to the boiling flux of heat. The current work stressed the absolute motion of warmth guaranteeing from the summation of each of the three segments of regular convection, upgraded convection and idle warmth transport for water and methanol with round mathematical formed. The hypotheses of air pocket development in nucleate pool bubbling hypothesize for a significant bit of warmth move to the air pocket happens by conduction through a fluid microlayer framed on the warmed surface has been thought of. The maximum deviation of error between the present analytical and experimental one for water, methanol, ethanol and benzene is 6.89%, 5.24%, 5.64% and 6.21% respectively. The highest divergence in the middle of the forecasted data and different (analytical and investigational) outcomes is found to be ±2.54. Results from many other investigators have also been compared for the better visualization of heat transfer correlations to the present one. The highest divergence in the middle of the forecasted data and investigational one for Nusselt number is observed to be ±3.27 along with the present analytical ones.

ACS Style

Ashwini Kumar; Aruna Kumar Behura; Dipen Kumar Rajak; Ravinder Kumar; Mohammad H. Ahmadi; Mohsen Sharifpur; Olusola Bamisile. Performance of heat transfer mechanism in nucleate pool boiling -a relative approach of contribution to various heat transfer components. Case Studies in Thermal Engineering 2020, 24, 100827 .

AMA Style

Ashwini Kumar, Aruna Kumar Behura, Dipen Kumar Rajak, Ravinder Kumar, Mohammad H. Ahmadi, Mohsen Sharifpur, Olusola Bamisile. Performance of heat transfer mechanism in nucleate pool boiling -a relative approach of contribution to various heat transfer components. Case Studies in Thermal Engineering. 2020; 24 ():100827.

Chicago/Turabian Style

Ashwini Kumar; Aruna Kumar Behura; Dipen Kumar Rajak; Ravinder Kumar; Mohammad H. Ahmadi; Mohsen Sharifpur; Olusola Bamisile. 2020. "Performance of heat transfer mechanism in nucleate pool boiling -a relative approach of contribution to various heat transfer components." Case Studies in Thermal Engineering 24, no. : 100827.

Journal article
Published: 17 December 2020 in Energy Reports
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This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.

ACS Style

Hanumant P. Jagtap; Anand K. Bewoor; Ravinder Kumar; Mohammad Hossein Ahmadi; Mamdouh El Haj Assad; Mohsen Sharifpur. RAM analysis and availability optimization of thermal power plant water circulation system using PSO. Energy Reports 2020, 7, 1133 -1153.

AMA Style

Hanumant P. Jagtap, Anand K. Bewoor, Ravinder Kumar, Mohammad Hossein Ahmadi, Mamdouh El Haj Assad, Mohsen Sharifpur. RAM analysis and availability optimization of thermal power plant water circulation system using PSO. Energy Reports. 2020; 7 ():1133-1153.

Chicago/Turabian Style

Hanumant P. Jagtap; Anand K. Bewoor; Ravinder Kumar; Mohammad Hossein Ahmadi; Mamdouh El Haj Assad; Mohsen Sharifpur. 2020. "RAM analysis and availability optimization of thermal power plant water circulation system using PSO." Energy Reports 7, no. : 1133-1153.

Original articles
Published: 08 December 2020 in Numerical Heat Transfer, Part A: Applications
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An attempt is made to improve the overall performances of channel heat exchangers. The techniques of baffles and nanofluids are combined to enhance the dynamic and thermal behaviors within the channel exchanger. Baffles under various attack angles are used as vortex generators. In addition, oil/multiwalled carbon nanotubes (MWCNT) is used as a working fluid. Both inclinations in the upstream and downstream directions were considered, referenced as Case A (UIB) and Case B (BIB), respectively. While the channel equipped with vertical baffles is referenced as Case C. The proposed models with combined techniques allowed a considerable enhancement in the overall efficiency. The comparison between the three cases revealed that the most significant value of thermal enhancement factor (TEF) of 5.634 was reached with vertical baffles (Case C) at the highest value of Reynolds number. When using inclined baffles, the 75° upstream attack angle (Case A) allowed the highest TEF of 4.814, compared with Case B.

ACS Style

Younes Menni; Ali J. Chamkha; Mahyar Ghazvini; Mohammad Hossein Ahmadi; Houari Ameur; Alibek Issakhov; Mustafa Inc. Enhancement of the turbulent convective heat transfer in channels through the baffling technique and oil/multiwalled carbon nanotube nanofluids. Numerical Heat Transfer, Part A: Applications 2020, 79, 311 -351.

AMA Style

Younes Menni, Ali J. Chamkha, Mahyar Ghazvini, Mohammad Hossein Ahmadi, Houari Ameur, Alibek Issakhov, Mustafa Inc. Enhancement of the turbulent convective heat transfer in channels through the baffling technique and oil/multiwalled carbon nanotube nanofluids. Numerical Heat Transfer, Part A: Applications. 2020; 79 (4):311-351.

Chicago/Turabian Style

Younes Menni; Ali J. Chamkha; Mahyar Ghazvini; Mohammad Hossein Ahmadi; Houari Ameur; Alibek Issakhov; Mustafa Inc. 2020. "Enhancement of the turbulent convective heat transfer in channels through the baffling technique and oil/multiwalled carbon nanotube nanofluids." Numerical Heat Transfer, Part A: Applications 79, no. 4: 311-351.

Journal article
Published: 26 October 2020 in Symmetry
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Heavy oil and bitumen supply the vast majority of energy resources in Canada. Different methods can be implemented to produce oil from such unconventional resources. Surfactants are employed as an additive to water/steam to improve an injected fluid’s effectiveness and enhance oil recovery. One of the main fractions in bitumen is asphaltene, which is a non-symmetrical molecule. Studies of interactions between surfactants, anionic, and non-anionic, and asphaltene have been very limited in the literature. In this paper, we employed molecular dynamics (MD) simulation to theoretically focus on the interactions between surfactant molecules and different types of asphaltene molecules observed in real oil sands. Both non-anionic and anionic surfactants showed promising results in terms of dispersant efficiency; however, their performance depends on the asphaltene architecture. Moreover, a hydrogen/carbon (H/C) ratio of asphaltenes plays an inevitable role in asphaltene aggregation behavior. A higher H/C ratio resulted in decreasing asphaltene aggregation tendency. The results of these studies will give a deep understanding of the interactions between asphaltene and surfactant molecules.

ACS Style

Mohammadali Ahmadi; Zhangxin Chen. Molecular Interactions between Asphaltene and Surfactants in a Hydrocarbon Solvent: Application to Asphaltene Dispersion. Symmetry 2020, 12, 1767 .

AMA Style

Mohammadali Ahmadi, Zhangxin Chen. Molecular Interactions between Asphaltene and Surfactants in a Hydrocarbon Solvent: Application to Asphaltene Dispersion. Symmetry. 2020; 12 (11):1767.

Chicago/Turabian Style

Mohammadali Ahmadi; Zhangxin Chen. 2020. "Molecular Interactions between Asphaltene and Surfactants in a Hydrocarbon Solvent: Application to Asphaltene Dispersion." Symmetry 12, no. 11: 1767.

Journal article
Published: 11 October 2020 in Journal of Natural Gas Science and Engineering
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The correlating capabilities of four machine learning methods, the ordinary kriging method, an adaptive neuro-fuzzy interference system (ANFIS), a multi-layer artificial neural network (ANN) and a Hybrid of Fuzzy logic and Genetic Algorithm (HFGA), as well as the thermodynamics-based approach of van der Waals-Platteeuw (vdWP) are compared for CO2 gas hydrates formed in the presence of thermodynamic promoters as well as for semi-clathrates formed from CO2. These systems were chosen for testing the three methods due to their potential relevance in CO2 capture and due to the expectation of them being computationally challenging. This is the first time that kriging has been tested for correlating gas hydrate equilibrium conditions. Different statistical indices, including the mean square error (MSE), an average absolute relative deviation (AARD), a correlation coefficient, and minimum and maximum errors, are employed to evaluate the performance of these methods. According to these performance indices, the ANFIS method performed the best among these methods; it predicted the equilibrium pressure with the highest accuracy. Finally, an outlier diagnosis is applied to the generated results to specify the reliability and uncertainty of the machine learning-based models. The simple-to-use machine learning tools are shown to be acceptable alternative to the vdWP methods and can be easily coupled with commercial simulation software to reduce calculation times while maintaining the accuracy.

ACS Style

Mohammadali Ahmadi; Zhangxin Chen; Matthew Clarke; Eugene Fedutenko. Comparison of Kriging, Machine Learning Algorithms and Classical Thermodynamics for Correlating the Formation Conditions for CO2 Gas Hydrates and Semi-Clathrates. Journal of Natural Gas Science and Engineering 2020, 84, 103659 .

AMA Style

Mohammadali Ahmadi, Zhangxin Chen, Matthew Clarke, Eugene Fedutenko. Comparison of Kriging, Machine Learning Algorithms and Classical Thermodynamics for Correlating the Formation Conditions for CO2 Gas Hydrates and Semi-Clathrates. Journal of Natural Gas Science and Engineering. 2020; 84 ():103659.

Chicago/Turabian Style

Mohammadali Ahmadi; Zhangxin Chen; Matthew Clarke; Eugene Fedutenko. 2020. "Comparison of Kriging, Machine Learning Algorithms and Classical Thermodynamics for Correlating the Formation Conditions for CO2 Gas Hydrates and Semi-Clathrates." Journal of Natural Gas Science and Engineering 84, no. : 103659.