Optimization of the exergy efficiency, exergy destruction, and engine noise index in an engine with two direct injectors using NSGA-II and artificial neural network

Direct Dual Fuel Stratification (DDFS) strategy is a novel Low Temperature Combustion (LTC) strategy that has comparable thermal efficiency to the Reactivity Controlled Compression Ignition (RCCI) strategy, while it offers more control over the combustion process and the rate of heat release. The DDFS strategy uses two direct injectors for the low- and high-reactivity fuels (gasoline and diesel) to benefit from the RCCI concept. In this study, the injection strategy of the injectors of a gasoline/diesel DDFS engine was optimized from the thermodynamic perspective to maximize exergy efficiency and minimize exergy destruction and an engine noise index. An artificial neural network was developed with 576 samples from a CFD code to predict the DDFS mode behavior, and the non-dominated sorting genetic algorithm (NSGA-II) was used to obtain the Pareto Front and the optimal solutions. Compared to the base case, the exergy efficiency of the optimal cases increased by up to 2%, exergy destruction and Peak Pressure Rise Rate (PPRR) reduced by about 2.3%, and 2 bar/deg, respectively, in the optimal solutions. NOX and soot emissions were reduced by 40% and 35%, respectively, in the best-case scenarios.

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Towards modeling of combined cooling, heating and power system with artificial neural network for exergy destruction and exergy efficiency prognostication of tri-generation components

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2015.06.008 ◽

2015 ◽

Vol 89 ◽

pp. 156-168 ◽

Cited By ~ 3

Author(s):

Hadi Taghavifar ◽

Simin Anvari ◽

Rahim Khoshbakhti Saray ◽

Shahram Khalilarya ◽

Samad Jafarmadar ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power System ◽

Exergy Efficiency ◽

Exergy Destruction ◽

Artificial Neural ◽

Combined Cooling

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Comparison of Optimization of Exergy Efficiency of a Crude Distillation Unit Using Artificial Neural Network (ANN) and Response Surface Methods (RSM)

Journal of Engineering Research and Reports ◽

10.9734/jerr/2020/v14i317123 ◽

2020 ◽

pp. 1-14

Author(s):

M. N. Braimah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Exergy Efficiency ◽

Operating Conditions ◽

Distillation Unit ◽

Optimum Operating ◽

Base Case ◽

Operating Variables ◽

Port Harcourt ◽

Artificial Neural

The study carried out simulation of the Crude Distillation Unit (CDU) of the New Port Harcourt Refinery (NPHR) and performed exergy analysis of the Refinery. The Crude Distillation Unit (CDU) of the New Port Harcourt refinery was simulated using HYSYS (2006.5). The Atmospheric Distillation Unit (ADU) which is the most inefficient unit and where major separation of the crude occurs was focused on. The simulation result was exported to Microsoft Excel Spreadsheet for exergy analysis. The ADU was optimized using statistical method and Artificial Neural Network. Box-Behnken model was applied to the sensitive operating variables that were identified. The statistical analysis of the RSM was carried out using Design Expert (6.0). Matlab software was used for the Artificial Neural Network. All the operating variables were combined to give the best optimum operating conditions. Exergy efficiency of the ADU was 51.9% and 52.4% when chemical exergy was included and excluded respectively. The optimum operating conditions from statistical optimization (RSM) are 586.1 K for liquid inlet temperature, 595.5 kPa for liquid inlet pressure and condenser pressure of 124 kPa with exergy efficiency of 69.6% which is 33.0% increment as compared to the base case. For the ANN optimization, the exergy efficiency of the ADU was estimated to be 70.6%. This gave an increase of 34.9% as compared to the base case. This study concluded that enormous improvement can be achieved both in design feasibility and improved efficiency if the feed operating parameters and other sensitive parameters are carefully chosen. Furthermore, ANN optimization gave better exergy efficiency of 70.6% than RSM optimization of 69.6%.

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Exergy Assessment of Single Stage Solar Adsorption Refrigeration System Using ANN

ISRN Mechanical Engineering ◽

10.5402/2012/915154 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

V. Baiju ◽

C. Muraleedharan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Exergy Efficiency ◽

Exergy Destruction ◽

Refrigeration System ◽

Adsorption Refrigeration ◽

Artificial Neural

A new approach based on artificial intelligence is proposed here for the exergy assessment of solar adsorption refrigeration system working with activated carbon-methanol pair. Artificial neural network model is used for the prediction of exergy destruction and exergy efficiency of each component of the system. Pressure, temperature and solar insolation are used as input variables for developing the artificial neural network model. The back propagation algorithm with three different variants such as CGP, SCG and LM are used in the network A and network B. The most suitable algorithm and the number of neurons in hidden layer are found as LM with 9 for network A and SCG with 17 for the Network B. The artificial neural network predicted results are compared with the calculated values of exergy destruction and exergy efficiency. The values of the exergy destruction and exergy efficiency of components (condenser, expansion device, evaporator, adsorbent bed, solar concentrator and overall system) are found to be close to 1. The RMS and COV values are found to be very low in all cases. The comparison of the results suggests that the artificial neural network provided results are within the acceptable range.

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Thermodynamic analysis and simulation of a new combined power and refrigeration cycle using artificial neural network

Thermal Science ◽

10.2298/tsci101102009f ◽

2011 ◽

Vol 15 (1) ◽

pp. 29-41 ◽

Cited By ~ 3

Author(s):

Abdolreza Fazeli ◽

Hossein Rezvantalab ◽

Farshad Kowsary

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Source ◽

Exergy Efficiency ◽

Combined Cycle ◽

Cycle Performance ◽

Working Fluid ◽

Refrigeration Cycle ◽

Water Mixture ◽

Artificial Neural

In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN) is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.

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Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

Sustainability ◽

10.3390/su132111654 ◽

2021 ◽

Vol 13 (21) ◽

pp. 11654

Author(s):

Roozbeh Vaziri ◽

Akeem Adeyemi Oladipo ◽

Mohsen Sharifpur ◽

Rani Taher ◽

Mohammad Hossein Ahmadi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mass Flow Rate ◽

Mass Flow ◽

Flow Rate ◽

Thermal Efficiency ◽

Exergy Efficiency ◽

Wire Mesh ◽

Double Pass ◽

Artificial Neural

Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature.

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Modelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network

International Journal of Low-Carbon Technologies ◽

10.1093/ijlct/ctaa033 ◽

2020 ◽

Author(s):

Zafer Utlu ◽

Mert Tolon ◽

Arif Karabuga

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Energy ◽

Ambient Temperature ◽

Exergy Analysis ◽

Energy System ◽

Exergy Efficiency ◽

Energy And Exergy ◽

Energy And Exergy Analysis ◽

Artificial Neural

Abstract The present study focuses on the organic Rankine cycle (ORC) integrated into an evacuated tube heat pipe (ETHP), whose systems are an alternative solar energy system to low-efficiency planary collectors. In this work, a detailed thermodynamic and artificial neural network (ANN) analysis was conducted to evaluate the solar energy system. One of the key parameters of sustainable approaches focused on exergy efficiency is application of thermal engineering. In addition to this, sustainable engineering approaches nowadays are a necessity for improving the efficiency of all of the engineering research areas. For this reason, the ANN model is used to forecast different types of energy efficiency problems in thermodynamic literature. The examined system consists of two main parts such as the ETHP system and the ORC system used for thermal energy production. With this system, it is aimed to evaluate energy and exergy analysis results by the ANN method in the case of integrating the ORC system to ETHP, which is one of the planar collectors suitable for the roofs of the buildings. Within the scope of this study, the exergy efficiency was evaluated on the developed ANN algorithm. The effect rates of parameters such as pressure, temperature and ambient temperature affecting the exergy efficiency of ORC integrated ETHP were calculated. Ambient temperature was found to be the most influential parameter on exergy efficiency. The exergy efficiency of the whole system has been calculated as ~23.39%. The most suitable BPNN architecture for this case study is recurrent networks with dampened feedback (Jordan–Elman nets). The success rate of the developed BPNN model is 95.4%.

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An approach using Artificial Neural Network and Genetic Algorithm for Day Trade Portfolio Selection

10.21528/cbic2021-88 ◽

2021 ◽

Author(s):

Paula Campigotto ◽

Omir Correia Alves Junior

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Portfolio Selection ◽

Financial Market ◽

Financial Strategy ◽

Nsga Ii ◽

Financial Investments ◽

Artificial Neural ◽

Investment Portfolios

In the financial market there are several types of investors, from the most conservative to the most daring, who are subject to greater risks in the expectation of greater returns on their investments. However, the concept of risk, in investment portfolios, makes it possible to measure it in different ways. This paper aims to present a method created to select portfolios for Day Trade financial investments using different metric risks, such as CVaR, EWMA and GARCH, and the ensemble of Genetic Algorithm NSGA-II and LSTM Artificial Neural Network, comparing it’s selected portfolios’ performance with another method which uses only NSGA-II and Buy and Hold financial strategy. The results show that the proposed method, with LSTM ANN achieved better returns in the year of 2019.

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Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Journal of Energy Research and Reviews ◽

10.9734/jenrr/2020/v5i430154 ◽

2020 ◽

pp. 26-38

Author(s):

M. N. Braimah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Product Quality ◽

Exergy Efficiency ◽

Oil Refinery ◽

Distillation Unit ◽

Optimum Operating ◽

Port Harcourt ◽

Artificial Neural ◽

Artificial Neural Network Ann

Background: Optimizing the process conditions of the crude distillation unit is a main challenge for each refinery. Optimization increases profit by producing the required range of distillates at maximum yield and at minimum cost. To achieve an acceptable control of product quality an artificial neural network (ANN) can be used. ANNs are used for engineering purposes, such as pattern recognition, forecasting, and data compression. In the petroleum refinery industry, ANN has been used as controller in for the crude distillation unit. The aim of the current study was to use ANN to optimize and achieve control of product quality of crude distillation unit of an oil refinery. Materials: The research was carried out using the following materials; The design flowchart and the operating data of the crude distillation unit of the New Port Harcourt refinery, Simulation software (HYSYS 2006.5) and Matlab for the ANN. Results: The ANN predicted the optimum operating conditions at which the atmospheric distillation unit (ADU) can operate with the least irreversibility and without changing the design and compromising the products quality. The corresponding exergy efficiency after optimization with ANN for the input variable combinations was 70.6% which was a great improvement because the exergy efficiency increased as compared to the base case of 51.9%. Conclusion: Optimization using ANN, improved the efficiency of the ADU with the least irreversibility and without changing the design and compromising the products quality.

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