Co-pyrolysis of municipal sewage sludge and microalgae Chlorella Vulgaris: Products' optimization; thermo-kinetic study, and ANN modeling

Co-pyrolysis of microalgae Chlorella vulgaris (MCV) and municipal sewage sludge (MSS) was performed in a fixed-bed reactor to explore the influence of temperature (400-600 C), mixing ratio (MCV/MSS = 0-1), and Argon flow rate of 0.20 to 0.80 L/min on pyrolysis products. The process was optimized using RSM to maximize bio-oil (BIO), minimize biochar (BIC), and biogas (BIG) yields. According to the ANOVA results, the mixing ratio has the most remarkable impact on BIO yield, and temperature has the highest influence on both BIC and BIG efficiency. By using numerical optimization, the optimum values of reaction parameters were T = 520 C, mixing ratio (MCV/MSS) = 0.82, and Argon flow rate of 0.55 L/min. The third, second, and third-order of chemical reaction models can fit the pure MSS, MCV, and mix pyrolysis utilizing the Coats-Redfern technique, respectively. Also, master plots approaches were used to identify dominant reaction mechanisms. The values of the E-alpha calculated from non-model fitting methods for MSS, MCV, and co-pyrolysis are obtained in the domain of 79.19-125.59, 182.41-194.73, and 149.55-216.61 kJ/mol, in that order. The presence of the synergetic and inhibitive influences generated by the co-pyrolysis of MSS and MCV has been demonstrated by the improved and reduced weight loss rate, respectively. Furthermore, artificial neural network (ANN) models with the architectures (3*8*3) and (2*8*1) were built to simulate and predict pyrolysis yields and the thermal decomposition of individual and co-pyrolysis process, demonstrated a strong agreement between the actual data and predicted data (R-2 >= 0.999) are considerably closer to 1.

Automated summary

Co-pyrolysis of microalgae Chlorella vulgaris and municipal sewage sludge was studied in a fixed-bed reactor to investigate the effects of temperature, mixing ratio, and Argon flow rate on pyrolysis products. The process was optimized using response surface methodology, and artificial neural network models were developed to simulate and predict pyrolysis yields. The study found that the mixing ratio had the most significant impact on bio-oil yield, while temperature had the highest influence on both biochar and biogas efficiency. The results demonstrate the potential for co-pyrolysis of microalgae and sewage sludge as a sustainable waste-to-energy conversion process.