Hydrothermal carbonization of agricultural residues: A case study of the farm residues -based biogas plants

Seyedsadr, Samar; Afif, Rafat Al; Pfeifer, Christoph

doi:10.1016/j.crcon.2018.06.001

Cited by 39 publications

(24 citation statements)

References 12 publications

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“…The energy content of the raw cornstalk was increased from 16.35 to 26.31 MJ/kg. A similar result was also observed for the HTC treatment of other biomass such as biogas sludge, barley, and maize silage, starch, municipal solid waste, and sewage sludge (Seyedsadr et al 2018;Kim et al 2014;Parshetti et al 2013;Hwang et al 2012;Sevilla and Fuertes 2009). Therefore, it can be concluded that temperature and residence time do influence the properties of the hydrochar as well as the raw biomass type.…”

Section: Introductionsupporting

confidence: 76%

Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

Abdulsalam

Lawal

Setsepu

et al. 2020

Preprint

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Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

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Section: Introductionsupporting

confidence: 76%

Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

Abdulsalam

Lawal

Setsepu

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionsupporting

confidence: 70%

Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

Abdulsalam

Lawal

Setsepu

et al. 2020

Bioresour. Bioprocess.

View full text Add to dashboard Cite

Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

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“…The potential synergy between hydrothermal carbonization and anaerobic digestion has been suggested, so far, by plenty of published studies [20,[30][31][32][33][34][35][36][37][38]. This synergy can be achieved in many ways.…”

Section: Introductionmentioning

confidence: 99%

Treatment of Liquid By-Products of Hydrothermal Carbonization (HTC) of Agricultural Digestate Using Membrane Separation

et al. 2020

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Agriculture affects both the quantity and the quality of water available for other purposes, which becomes problematic, especially during increasingly frequent severe droughts. This requires tapping into the resources that are typically neglected. One such resource is a by-product of anaerobic digestion, in which moisture content typically exceeds 90%. Application of hydrothermal carbonization process (HTC) to this residue could partially remove organic and inorganic material, improve dewatering, decrease the overall solid mass, sanitize the digestate, change its properties, and eliminate problems related with emissions of odors from the installation. However, a significant gap still exists in terms of the dewatering of the hydrochars and the composition of the effluents. This work presents results of experimental investigation focused on the removal of organic compounds from the HTC effluent. Results of qualitative and quantitative analysis of liquid by-products of HTC of the agricultural digestate showed that acetic acid, 3-pyridinol, 1-hydroxyacetone, and 1,3-propanediol were the main liquid organic products of the process. Application of ultrafiltration process with the use of 10 kDa membrane for liquid HTC by-product treatment allows for the reduction of chemical oxygen demand up to 30%, biological oxygen demand up to 10%, and dissolved organic carbon up to 21%.

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Hydrothermal carbonization of agricultural residues: A case study of the farm residues -based biogas plants

Cited by 39 publications

References 12 publications

Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

Treatment of Liquid By-Products of Hydrothermal Carbonization (HTC) of Agricultural Digestate Using Membrane Separation

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