2020American Scientific Research Journal for Engineering, Technology, and Sciences (Global Society of Scientific Research and Researchers)Open access

Optimization of Methane Production from Macroalgae Feedstock using Multivariate Technique under Mesophilic and Thermophilic Conditions

Edward Membere

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Abstract

A multivariate technique was used to optimize methane production from anaerobic digestion of macroalgae under mesophilic and thermophilic conditions. To evaluate the effects and interaction of three reaction variables: COD, VFA, and ammonia on methane production, their data recorded in a time order were subjected to fit and multiple regression analysis, which generated a second order quadratic polynomial equation used to predict the optimized methane production. The ANOVA results showed the developed model for the mesophilic (p< 0.003) and thermophilic (p< 0.000) reactors are significant. Their R2 values of 0.97 and 0.99 suggest it was suitable for interpreting the experimental data set and adjusted R2 of (0.91 and 0.97) indicates good regression models. The interaction terms for mesophilic and thermophilic reactors, has a positive influence on methane production compared to other terms. The model predicted the optimal reactors conditions, derived as X1: COD = 6.6 g L-1, X2: VFAs = 2.8 g L-1, X3: Ammonia = 1.3 g L-1 for the mesophilic reactor, and X1: COD = 6.7 g L-1, X2: VFAs = 2.5 g L-1, X3: Ammonia = 1.1 g L-1 for the thermophilic reactor.

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A multivariate technique was used to optimize methane production from anaerobic digestion of macroalgae under mesophilic and thermophilic conditions. To evaluate the effects and interaction of three reaction variables: COD, VFA, and ammonia on methane production, their data recorded in a time order were subjected to fit and multiple regression analysis, which generated a second order quadratic polynomial equation used to predict the optimized methane production. The ANOVA results showed the developed model for the mesophilic (p< 0.003) and thermophilic (p< 0.000) reactors are significant. Their R2 values of 0.97 and 0.99 suggest it was suitable for interpreting the experimental data set and adjusted R2 of (0.91 and 0.97) indicates good regression models. The interaction terms for mesophilic and thermophilic reactors, has a positive influence on methane production compared to other terms. The model predicted the optimal reactors conditions, derived as X1: COD = 6.6 g L-1, X2: VFAs = 2.8 g L-1, X3: Ammonia = 1.3 g L-1 for the mesophilic reactor, and X1: COD = 6.7 g L-1, X2: VFAs = 2.5 g L-1, X3: Ammonia = 1.1 g L-1 for the thermophilic reactor.

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Available abstract

A multivariate technique was used to optimize methane production from anaerobic digestion of macroalgae under mesophilic and thermophilic conditions. To evaluate the effects and interaction of three reaction variables: COD, VFA, and ammonia on methane production, their data recorded in a time order were subjected to fit and multiple regression analysis, which generated a second order quadratic polynomial equation used to predict the optimized methane production. The ANOVA results showed the developed model for the mesophilic (p< 0.003) and thermophilic (p< 0.000) reactors are significant. Their R2 values of 0.97 and 0.99 suggest it was suitable for interpreting the experimental data set and adjusted R2 of (0.91 and 0.97) indicates good regression models. The interaction terms for mesophilic and thermophilic reactors, has a positive influence on methane production compared to other terms. The model predicted the optimal reactors conditions, derived as X1: COD = 6.6 g L-1, X2: VFAs = 2.8 g L-1, X3: Ammonia = 1.3 g L-1 for the mesophilic reactor, and X1: COD = 6.7 g L-1, X2: VFAs = 2.5 g L-1, X3: Ammonia = 1.1 g L-1 for the thermophilic reactor.

Key concepts: Mesophile, Thermophile, Methane, Anaerobic digestion, Raw material, Chemistry, Ammonia, Food science

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Optimization of Methane Production from Macroalgae Feedstock using Multivariate Technique under Mesophilic and Thermophilic Conditions — Research Paper | ScholarLens