Influence of the Construction Materials Properties of the Biodigester on the Biogas Production and Electricity Generated by the Slaughterhouse Waste


Ebeya C. C. , Ali M. M. , Sidibba A., Yetilmezsoy K., Kıyan E., Kane C. S. E. , ...More

INTERNATIONAL JOURNAL OF DESIGN AND NATURE AND ECODYNAMICS, vol.17, pp.513-520, 2022 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 17
  • Publication Date: 2022
  • Doi Number: 10.18280/ijdne.170404
  • Journal Name: INTERNATIONAL JOURNAL OF DESIGN AND NATURE AND ECODYNAMICS
  • Journal Indexes: Scopus, CAB Abstracts
  • Page Numbers: pp.513-520

Abstract

The objective of this study was to carry out a comparative analysis of the influence of the properties of the construction materials of the biodigester based on three different materials (steel, plastic PVC, and concrete) to predict the rate of biogas production from the anaerobic digestion of slaughterhouse waste. The input parameters were substrate temperature, ambient temperature, biogas temperature, biodigester temperature, specific biogas production rate, and material properties. A thermal model was developed using MATLAB® software to predict biogas production, with readily available input data for an unheated, uninsulated, and partially buried biodigester. The results obtained showed that the temperatures and the average daily biogas productions were higher for the steel biodigester (1.5 ± 0.12 m3/day at 36 ± 2℃) than those produced from the PVC (1.3 ± 0.1 m3/day at 31 ± 1.5℃) and concrete (1.2 ± 0.05 m3/day at 27 ± 2℃) biodigesters. Moreover, the production of electricity for a steel biodigester (14.64 kWh) was found to be greater than that produced from the PVC (12.81 kWh) and concrete (10.98 kWh) biodigesters. The results showed that the properties of the construction materials of the digester had a significant influence on the temperature and production of biogas, and therefore on the production of electricity. On the other hand, among the three materials studied, steel was the material, which yielded the best results. The proposed model gave rRMSE values between 7.4 and 8.3% and R2 between 0.92 and 0.96.