Optimization of Cobalt Nanoparticles for Biogas Enhancement from Green Algae Using Response Surface Methodology

• Published in: PERIODICA POLYTECHNICA-CHEMICAL ENGINEERING
• Main Authors: Zaidi, Asad A.; Khan, Sohaib Z.; Naseer, Muhammad Nihal; Almohammadi, Hamad; Asif, Muhammad; Wahab, Yasmin Abdul; Islam, Mohammad Aminul; Johan, Mohd Rafie; Hussin, Hanim
• Format: Article
• Language: English
• Published: BUDAPEST UNIV TECHNOLOGY ECONOMICS 2023
• Subjects: Engineering
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001151608200010

Organic matter may be converted to energy through various methods, but the most preferable one is the Anaerobic Digestion (AD), specifically for biogas production. In sustainable bioenergy production, it can undoubtedly be called one of the most widely used methods from the various feedstock. Over the past years, algae waste has become an increasingly acute environmental problem but luckily it can be used as feedstock to produce bioenergy. In order to improve the energy productivity of green algae, this study is focused on the introduction of cobalt (Co) nanoparticles (NPs) in the AD process. The concentration of Co NPs was optimized using response surface methodology (RSM). Mesophilic temperature range (25-45 degrees C), initial pH (5-9) and Co NPs dosage (0.5-2 mg/L) were selected as the independent variables for RSM. The results indicated that at optimized values (Co NPs concentration = 1 mg/L, initial pH = 7, and digestion temperature = 35 degrees C) produced the highest biogas yield of 298 ml. An experiment was carried out at optimized conditions to explore the effect on biogas production. The results showed that Co NPs had a positive influence on biogas yield. The low concentrations achieved higher biogas production as compared to higher ones. A maximum biogas yield of 678 mL is achieved by Co NPs (1 mg/L). AD performance was further evaluated by the modified Gompertz model. Different kinetic parameters were calculated. The values of the performance indicators confirmed that the mathematical model fitted well with experimental data.