Analysis of the biogas productivity from dry anaerobic digestion of organic fraction of municipal solid waste
- Authors: Matheri, Anthony Njuguna , Sethunya, Vuiswa Lucia , Belaid, Mohamed , Muzenda, Edison , Ntuli, Freeman
- Date: 2017
- Subjects: BMP , Biodegradable , Co-digestion
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/380169 , http://hdl.handle.net/10210/241025 , uj:24804 , Citation: Matheri, A.N. 2017. Analysis of the biogas productivity from dry anaerobic digestion of organic fraction of municipal solid waste.
- Description: Abstract: In this study, it was observed that in experimental work under laboratory scale using conventional biomethane potential (BMP) analyser under the mesophilic optimum temperature of 37 0C and pH of 7. Organic fraction municipality solid waste (OFMSW) inoculated with cow manure had higher biodegradability rate leading to high methane production under shorter hydraulic retention rate. The co-digestion of OFMSW and cow manure stabilises conditions in digestion process such as carbon to nitrogen (C: N) ratio in the substrate mixtures as well as macro and micronutrients, pH, inhibitors or toxic compounds, dry matter and thus increasing methane production. It was concluded that the organic waste generated in the municipality co-digested with manures to produce methane can be used as a source of sustainable renewable energy.
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Performance prediction of trace metals and cod in wastewater treatment using artificial neural network.
- Authors: Matheri, Anthony Njuguna , Ntuli, Freeman , Ngila, Jane Catherine , Seodigeng, Tumisang , Zvinowanda, Caliphs
- Date: 2021
- Subjects: Artificial intelligence , Artificial neural network , Genetic algorithms
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/489260 , uj:44604 , Citation: Matheri, A.N., Ntuli, F., Ngila, J.C., Seodigeng, T. and Zvinowanda, C., 2021. Performance prediction of trace metals and cod in wastewater treatment using artificial neural network. Computers & Chemical Engineering, 149, p.107308.
- Description: Abstract: Artificial intelligence is finding its ways into the mainstream of day-to-day operations. Novel AI application techniques such as the artificial neural network (ANN), fuzzy logic, genetic algorithms and expert systems have gained popularity in the fourth industrial revolution era. Due to the chemical composition, inherent complexity, incoherent flow rate and higher safety factor in the effective operation of the biological wastewater treatment process, the AI-based model was extensively tested in managing the wastewater treatment operations. The interrelationship between COD and trace metals was studied using AI-based prediction model with ANNs incorporated in MATLAB. Supervised learning algorithm was used for training the ANNs and to relate input data to output data. The training was aimed at estimating, validating, predicting the parameters by an error function minimization. The goodness of the prediction was attained with the coefficient of determination (R2) of 0.98-0.99, sum of square error (SSE) 0.00029-0.1598, room mean-square error (RMSE) of 0.0049-0.8673, mean squared error (MSE) 2.7059e-14 to 2.3175e-15. The ANNs models were found to be a robust tool for predicting WWTP performance. The predictive approaches can be used in the prediction of environmental management and other emerging technologies. This will meet the cost-effectiveness, effective environmental and technical criteria with a wide range of big-data support and implementation of the sustainable development goals, circular bio-economy and industry 4.0.
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Sludge to energy recovery dosed with selected trace metals additives in anaerobic digestion processes
- Authors: Matheri, Anthony Njuguna , Ntuli, Freeman , Ngila, Jane Catherine
- Date: 2020
- Subjects: Additive , Anaerobic digestion , Biomethane
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/460010 , uj:40920 , Citation: Matheri, A.N., Ntuli, F. & Ngila, J.C. 2020. Sludge to energy recovery dosed with selected trace metals additives in anaerobic digestion processes.
- Description: Abstract: The energy demand is ever rising with population increase and technology evolution. Coal consumption in South Africa is estimated to be 86% of the total energy demand. It has a high magnitude of environmental pollution and contributes to climate change. This calls for cleaner, reliable, sustainable, decarbonized, decentralized, affordable, digitized with the diversification of energy mix. The study aimed at investigating the impact of dosing selected trace metals (Ni, Co, Cu) as an additive to the substrate in the sludge to energy recovery using anaerobic processes. Sewage sludge and cellulose were used as a substrate. The biomethane potential study was carried from a 500 ml batch automated bio-digester at a mesophilic temperature of 37oC and a substratum-to-inoculum ratio (2:1) of the organic load rate. The dosed micro-nutrients acted as microbial-agents responsible for the anaerobic digestion of the feedstock. Cellulose and sludge at 0.25 mg/L (Ni) recorded the highest production of the biomethane. Cellulose inoculated with cobalt had better biomethane production at 0.02 mg/L until 0.05 mg/L. High production of biomethane was observed at the substrate with a copper concentration of 4.5 mg/L. Adding trace metals to microbial cell surroundings stimulated microbial activity and prevented the accumulation of the fatty acids. However, high concentrations beyond threshold resulted in inhibition, toxicity to the microbial-growth, which was reflected in the reduction of the production of biomethane.
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Waste to energy bio-digester selection and design model for the organic fraction of municipal solid waste
- Authors: Matheri, Anthony Njuguna , Mbohwa, Charles , Ntuli, Freeman , Belaid, Mohamed , Seodigeng, Tumisang , Ngila, Jane Catherine , Njenga, Cecilia Kinuthia
- Date: 2017
- Subjects: Anaerobic digestion , Bio-digester , Mesophilic temperature
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/250904 , uj:26157 , Citation: Matheri, A.N. et al. 2017. Waste to energy bio-digester selection and design model for the organic fraction of municipal solid waste.
- Description: Abstract: Please refer to full text to view abstract
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