Performance of Fuzzy Inference System for Calorific Value Predicting by Using The Mamdani Method

Viska Inda Variani

Abstract

It is well known that the energy content of fuel is characterized by its calorific value which is usually measured by using a bomb calorimeter. In many analysis, the calorific value is usually related with the proximate analysis results. In this study, the calorific value predicting program based on the proximate analysis data using the fuzzy inference system with Mamdani method has been developed. In our analysis, the proximate analysis data of moisture and volatile matter contents are used as input and the calorific value as output. The results showed that the influence of the moisture content is more dominant in determining the calorific value of fuel than volatile matter content. The performance of the calorific value predicting program also shown that the prediction error is about 0.75% to 5.2%. The obtained calorific value is well reproduced the experimental data.

Keywords

calorific value; moisture content; volatile matter content; fuzzy inference system; Mamdani method

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References

Gebrehiwot, G., Weldegebriel, D., & G. Haileslassi. 2019. Production and characterization of biomass briquette from sawdust and cow dung admixture for domestic cooking applications in northern Ethiopia. Int. J. Renew. Energy Its Commer, Vol. 5, No. 2, pp. 49–57.

O. U. C, A. C. N, A. Samuel, & S. J. E. 2017. Proximate analysis and performance evaluation of selected blends of biomass briquettes. J. enggineering Appl. Sci., vol. 10, pp. 144–161.

Sunardi, Djuanda, & Mandra, M. A. S. 2019. Characteristics of charcoal briquettes from agricultural waste with compaction pressure and particle size variation as alternative fuel’, Int. Energy J., vol. 19, no. 3, pp. 139–147.

Ajimotokan, H. A., Ehindero, A. O., Ajao, K. S., Adeleke, A. A., Ikubanni, P. P., & Shuaib-Babata, Y. L., 2019. Combustion characteristics of fuel briquettes made from charcoal particles and sawdust agglomerates. Sci. African, Vol. 6.

Suryaningsih, S., Resitasari, R., & Nurhilal, O., 2019. Analysis of biomass briquettes based on carbonized rice husk and jatropha seed waste by using newspaper waste pulp as an adhesive material. Journal of Physics: Conference Series.

Onukak, I. E., Mohammed-Dabo, I. A., Ameh, A. O., Okoduwa, S. I. R., & Fasanya, O. O., 2017. Production and characterization of biomass briquettes from tannery solid waste. Recycling, Vol. 2, No. 17.

ÖzyuǧUran, A., & Yaman, S. 2017. Prediction of calorific value of biomass from proximate analysis. Energy Procedia, Vol. 107, pp. 130–136.

Feng, Q., Zhang, J., Zhang, X., & Wen, S., 2015. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Process. Technol.

Mandavgade, N. K., Jaju, S. B., & Lakhe, R. R. 2012. Determination of uncertainty in gross calorific value of coal using bomb calorimeter. Int. J. Meas. Technol. Instrum. Eng, Vol. 1, No. 4, pp. 45–52.

Açikkar, M. & Sivrikaya, O. 2018. Prediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks. Turkish J. Electr. Eng. Comput. Sci.

Patel, S. U., Kumar, B. J., Badhe, Y. P., Sharma, B. K., Saha, S., Biswas, S., Chaudhury, A., Tambe, S.S., & Kulkarni, B. D. 2007. Estimation of gross calorific value of coals using artificial neural networks. Fuel, Vol. 86, No. 3, pp. 334-344.

Rafezi, F., Jorjani, E., & Karimi, S. 2011. Adaptive neuro-fuzzy inference system prediction of calorific value based on the analysis of US coals. Artif. Neural Networks - Ind. Control Eng. Appl., 169-182.

Erol, M., Haykiri-Acma, H., & Küçükbayrak, S. (2010). Calorific value estimation of biomass from their proximate analyses data. Renewable energy, Vol. 35, No. 1, pp. 170-173.

Variani, V. I. 2021. Calorific value predicting based on moisture and volatile matter contents using fuzzy inference system. Journal of Physics: Conference Series, Vol. 1825, No. 1, pp. 012006.

Mamdani, E. H., & Assilian, S. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man. Mach. Stud, Vol. 7, No. 1, pp. 1–13.

Plebankiewicz, E. 2018. Model of predicting cost overrun in construction projects. Sustain, Vol. 10.

Mohammadi, A., Alimardani, R., Akbarnia, A., & Akram, A. 2012 Modeling of draft force variation in a winged share tillage tool using fuzzy table look-up scheme. Agric. Eng. Int. CIGR J.

Moghbeli, F., Langarizadeh, M., Kiavar, M., Nikpajouh, A., & Khatibi, T. 2018 Expert triage system in cardiology emergency department. Int. J. Comput. Sci. Netw. Secur, Vol. 18, No. 10.

Bhatnagar, R., kanti Ghose, M., & Bhattacharjee, V. 2011. Selection of defuzzification method for predicting the early stage software effort using Mamdani FIS. Commun. Comput. Inf. Sci, Vol. 250, No. 9, pp. 375–381.

Jung, H. Y., Leem, S., Lee, S., & Park, T. 2016. A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction. Comput. Biol. Chem. Vol. 65, pp. 193–202.

Sunjana. 2019. Prediction of production using the fuzzy mamdani inference method. Int. J. Adv. Sci. Technol, Vol. 28, No. 6, pp. 136–139.

Ayuningtias, L. P., Irfan, M., & Jumadi, J. 2017. Analisa perbandingan logic fuzzy metode Tsukamoto, Sugeno dan Mamdani (Studi kasus : Prediksi jumlah pendaftar mahasiswa baru fakultas sains dan teknologi universita Islam Negri sunan Gunung Djati Bandung). J. Tek. Inform, Vol. 10, No. 1, Hal. 9–16.

Sabindo, L. O., Kadir., & Hasbi, M. 2020. Pengaruh variasi ukuran mesh terhadap nilai kalor briket arang tempurung kelapa. Vol. 5, No. 1, pp. 1–8.

Nurhilal, O., & Suryaningsih, S. 2018. Pengaruh komposisi campuran sabut dan tempurung kelapa terhadap nilai kalor biobriket dengan perekat molase. J. Ilmu dan Inov. Fis, Vol. 2, No. 1, pp. 8–14.

DOI: https://doi.org/10.13057/ijap.v11i1.44122

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