Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing
Efficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and iterative simulations, often resulting in suboptimal solutions due to the complex...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24016769 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841555737054019584 |
|---|---|
| author | Manimegalai T Anitha Gopalan Vanmathi Murugesan Jayant Giri Praveen Barmavatu Praveenkumar T R Dinesh Mavaluru Rafath Samrin |
| author_facet | Manimegalai T Anitha Gopalan Vanmathi Murugesan Jayant Giri Praveen Barmavatu Praveenkumar T R Dinesh Mavaluru Rafath Samrin |
| author_sort | Manimegalai T |
| collection | DOAJ |
| description | Efficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and iterative simulations, often resulting in suboptimal solutions due to the complex and nonlinear nature of heat transfer phenomena. In this study, the research proposes a novel approach to enhance heat exchanger design using an autoencoder model for predicting both efficiency and cost. The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. By learning the underlying patterns and relationships within the data, the autoencoder can effectively capture the nonlinear mappings between design parameters and performance metrics. Through extensive validation and testing, the proposed autoencoder model demonstrates superior accuracy in predicting heat exchanger efficiency and cost compared to conventional methods. Furthermore, the model enables rapid exploration of design alternatives and sensitivity analysis, facilitating informed decision-making in the design phase. By leveraging machine learning techniques, this approach offers a promising avenue for advancing heat exchanger design towards higher efficiency and lower cost in chemical processing applications. The framework demonstrates considerable promise in bolstering efficiency and enhancing economic viability, boasting a correlation coefficient of 0.98171 and a normalized root mean square error (NRMSE) of 0.001523. |
| format | Article |
| id | doaj-art-41051bc09227463194fb746c5c474222 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-41051bc09227463194fb746c5c4742222025-01-08T04:52:46ZengElsevierCase Studies in Thermal Engineering2214-157X2025-01-0165105645Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processingManimegalai T0Anitha Gopalan1Vanmathi Murugesan2Jayant Giri3Praveen Barmavatu4Praveenkumar T R5Dinesh Mavaluru6Rafath Samrin7School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, Tamilnadu, IndiaDepartment of Electronics and Communication Engineering, School of Electrical and Communication Sciences, B S Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, Tamilnadu, IndiaDepartment of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, IndiaDepartment of Mechanical Engineering, Faculty of Engineering, Universidad Tecnológica Metropolitana, Av. José Pedro Alessandri 1242, Santiago, ChileDepartment of Civil Engineering, Graphic Era Deemed to Be University, Dehradun, 248002, Uttarakhand, India; Corresponding author.Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi ArabiaEfficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and iterative simulations, often resulting in suboptimal solutions due to the complex and nonlinear nature of heat transfer phenomena. In this study, the research proposes a novel approach to enhance heat exchanger design using an autoencoder model for predicting both efficiency and cost. The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. By learning the underlying patterns and relationships within the data, the autoencoder can effectively capture the nonlinear mappings between design parameters and performance metrics. Through extensive validation and testing, the proposed autoencoder model demonstrates superior accuracy in predicting heat exchanger efficiency and cost compared to conventional methods. Furthermore, the model enables rapid exploration of design alternatives and sensitivity analysis, facilitating informed decision-making in the design phase. By leveraging machine learning techniques, this approach offers a promising avenue for advancing heat exchanger design towards higher efficiency and lower cost in chemical processing applications. The framework demonstrates considerable promise in bolstering efficiency and enhancing economic viability, boasting a correlation coefficient of 0.98171 and a normalized root mean square error (NRMSE) of 0.001523.http://www.sciencedirect.com/science/article/pii/S2214157X24016769Heat exchanger designAutoencoder modelEfficiency predictionCost predictionChemical processing optimization |
| spellingShingle | Manimegalai T Anitha Gopalan Vanmathi Murugesan Jayant Giri Praveen Barmavatu Praveenkumar T R Dinesh Mavaluru Rafath Samrin Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing Case Studies in Thermal Engineering Heat exchanger design Autoencoder model Efficiency prediction Cost prediction Chemical processing optimization |
| title | Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| title_full | Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| title_fullStr | Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| title_full_unstemmed | Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| title_short | Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| title_sort | enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing |
| topic | Heat exchanger design Autoencoder model Efficiency prediction Cost prediction Chemical processing optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X24016769 |
| work_keys_str_mv | AT manimegalait enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT anithagopalan enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT vanmathimurugesan enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT jayantgiri enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT praveenbarmavatu enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT praveenkumartr enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT dineshmavaluru enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing AT rafathsamrin enhancingheatexchangerdesignusingautoencodermodelforpredictingefficiencyandcostinchemicalprocessing |