IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222023076.html
   My bibliography  Save this article

Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends

Author

Listed:
  • Zandie, Mohammad
  • Ng, Hoon Kiat
  • Gan, Suyin
  • Muhamad Said, Mohd Farid
  • Cheng, Xinwei

Abstract

In this research, a multi-input multi-output artificial neural network (MIMO-ANN) is developed, in which 14 features associated with the engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline mixtures are meant to be modelled by a diverse combination of engine/combustion parameters. The selected targets comprise brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), combustion efficiency, coefficient of variance (COV), NOx, CO2, CO and HC emissions, exhaust temperature (Texh), in-cylinder pressure (Pcyl), maximum pressure rise rate (MPRR), heat release rate (HRR), combustion duration (CD) and ignition delay (ID). The inputs variables entail the load, speed, compression ratio, gasoline, biodiesel and diesel ratios, crank angle (CA), injection temperature (Tinj), injection pressure (Pinj), brake mean effective pressure (BMEP) and start of injection (SOI). Sensitivity analysis and outlier detection are applied in order to eliminate less-effective inputs/data points. The prepared data sets are then used to train and test the ANN model, in conjunction with benchmarking the model outcomes using coefficient of determination (R2), average absolute relative deviation (AARD) and relative mean squared errors (RMSE). The R2 ranged within 0.9804–0.9998, which is close to unity, proving that the proposed network is accurately capable of predicting the intended combustion characteristics.

Suggested Citation

  • Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023076
    DOI: 10.1016/j.energy.2022.125425
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222023076
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125425?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    2. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    3. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    4. Hoseinpour, Marziyeh & Sadrnia, Hassan & Tabasizadeh, Mohammad & Ghobadian, Barat, 2017. "Energy and exergy analyses of a diesel engine fueled with diesel, biodiesel-diesel blend and gasoline fumigation," Energy, Elsevier, vol. 141(C), pages 2408-2420.
    5. Zuo, Lei & Wang, Junfeng & Mei, Deqing & Dai, Shengchao & Adu-Mensah, Derick, 2022. "Experimental investigation on combustion and (regulated and unregulated) emissions performance of a common-rail diesel engine using partially hydrogenated biodiesel-ethanol-diesel ternary blend," Renewable Energy, Elsevier, vol. 185(C), pages 1272-1283.
    6. E, Jiaqiang & Pham, Minhhieu & Zhao, D. & Deng, Yuanwang & Le, DucHieu & Zuo, Wei & Zhu, Hao & Liu, Teng & Peng, Qingguo & Zhang, Zhiqing, 2017. "Effect of different technologies on combustion and emissions of the diesel engine fueled with biodiesel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 620-647.
    7. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    8. Xu, Yong & Li, Shanshan & Zhou, Xiaoxiao & Shahzad, Umer & Zhao, Xin, 2022. "How environmental regulations affect the development of green finance: Recent evidence from polluting firms in China," Renewable Energy, Elsevier, vol. 189(C), pages 917-926.
    9. Fayad, Mohammed A. & Tsolakis, Athanasios & Martos, Francisco J., 2020. "Influence of alternative fuels on combustion and characteristics of particulate matter morphology in a compression ignition diesel engine," Renewable Energy, Elsevier, vol. 149(C), pages 962-969.
    10. Prakash, T. & Geo, V. Edwin & Martin, Leenus Jesu & Nagalingam, B., 2018. "Effect of ternary blends of bio-ethanol, diesel and castor oil on performance, emission and combustion in a CI engine," Renewable Energy, Elsevier, vol. 122(C), pages 301-309.
    11. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    12. Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
    13. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2020. "Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends," Renewable Energy, Elsevier, vol. 149(C), pages 951-961.
    14. Domínguez-Sáez, Aida & Rattá, Giuseppe A. & Barrios, Carmen C., 2018. "Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression," Energy, Elsevier, vol. 149(C), pages 675-683.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    2. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    3. Podlasek, Szymon & Jankowski, Marcin & Bałazy, Patryk & Lalik, Krzysztof & Figaj, Rafał, 2024. "Application of ANN control algorithm for optimizing performance of a hybrid ORC power plant," Energy, Elsevier, vol. 306(C).
    4. Zhu, Yizi & He, Zhixia & Xuan, Tiemin & Shao, Zhuang, 2024. "An enhanced automated machine learning model for optimizing cycle-to-cycle variation in hydrogen-enriched methanol engines," Applied Energy, Elsevier, vol. 362(C).
    5. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Ji & Wu, Dawei & Mohammadsami Attar, Hassan & Xu, Hongming, 2022. "Geometric neuro-fuzzy transfer learning for in-cylinder pressure modelling of a diesel engine fuelled with raw microalgae oil," Applied Energy, Elsevier, vol. 306(PA).
    2. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    3. Wenbo Ai & Haeng Muk Cho, 2024. "Predictive Models for Biodiesel Performance and Emission Characteristics in Diesel Engines: A Review," Energies, MDPI, vol. 17(19), pages 1-25, September.
    4. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    5. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    6. Viswanathan, Vinoth Kannan & Kaladgi, Abdul Razak & Thomai, Pushparaj & Ağbulut, Ümit & Alwetaishi, Mamdooh & Said, Zafar & Shaik, Saboor & Afzal, Asif, 2022. "Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends," Renewable Energy, Elsevier, vol. 198(C), pages 549-567.
    7. Krishnamoorthi, M. & Malayalamurthi, R. & Sakthivel, R., 2019. "Optimization of compression ignition engine fueled with diesel - chaulmoogra oil - diethyl ether blend with engine parameters and exhaust gas recirculation," Renewable Energy, Elsevier, vol. 134(C), pages 579-602.
    8. Ghadikolaei, Meisam Ahmadi & Wong, Pak Kin & Cheung, Chun Shun & Ning, Zhi & Yung, Ka-Fu & Zhao, Jing & Gali, Nirmal Kumar & Berenjestanaki, Alireza Valipour, 2021. "Impact of lower and higher alcohols on the physicochemical properties of particulate matter from diesel engines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    9. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    10. Jagtap, Sharad P. & Pawar, Anand N. & Lahane, Subhash, 2020. "Improving the usability of biodiesel blend in low heat rejection diesel engine through combustion, performance and emission analysis," Renewable Energy, Elsevier, vol. 155(C), pages 628-644.
    11. Satar Bakhsh & Md Shabbir Alam & Wei Zhang, 2024. "Green finance and Sustainable Development Goals: is there a role for geopolitical uncertainty?," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-30, August.
    12. Liu, Teng & E, Jiaqiang & Yang, W.M. & Deng, Yuangwang & An, H. & Zhang, Zhiqing & Pham, Minhhieu, 2018. "Investigation on the applicability for reaction rates adjustment of the optimized biodiesel skeletal mechanism," Energy, Elsevier, vol. 150(C), pages 1031-1038.
    13. E, Jiaqiang & Pham, MinhHieu & Deng, Yuanwang & Nguyen, Tuannghia & Duy, VinhNguyen & Le, DucHieu & Zuo, Wei & Peng, Qingguo & Zhang, Zhiqing, 2018. "Effects of injection timing and injection pressure on performance and exhaust emissions of a common rail diesel engine fueled by various concentrations of fish-oil biodiesel blends," Energy, Elsevier, vol. 149(C), pages 979-989.
    14. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    15. Liu, Yang & Cheng, Xiaobei & Qin, Longjiang & Wang, Xin & Yao, Junjie & Wu, Hui, 2020. "Experimental investigation on soot formation characteristics of n-heptane/butanol isomers blends in laminar diffusion flames," Energy, Elsevier, vol. 211(C).
    16. Muhssen, Hassan Sadah & Masuri, Siti Ujila & Sahari, Barkawi Bin & Hairuddin, Abdul Aziz, 2021. "Design improvement of compressed natural gas (CNG)-Air mixer for diesel dual-fuel engines using computational fluid dynamics," Energy, Elsevier, vol. 216(C).
    17. Li, Shanshan & Long, Fang & Long, Litao, 2022. "Resources curse and sustainable development revisited: Evaluating the role of remittances for China," Resources Policy, Elsevier, vol. 79(C).
    18. Sultana, Ayesha & Alam, Md. Mehebub & Ghosh, Sujoy Kumar & Middya, Tapas Ranjan & Mandal, Dipankar, 2019. "Energy harvesting and self-powered microphone application on multifunctional inorganic-organic hybrid nanogenerator," Energy, Elsevier, vol. 166(C), pages 963-971.
    19. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    20. Zharova, P.A. & Chistyakov, A.V. & Shapovalov, S.S. & Pasynskii, A.A. & Tsodikov, M.V., 2019. "Original Pt-Sn/Al2O3 catalyst for selective hydrodeoxygenation of vegetable oils," Energy, Elsevier, vol. 172(C), pages 18-25.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023076. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.