Parametric study of single and double stage membrane configuration in methane enrichment process

Operational study of a biogas upgrading plant with cleaning and methane (CH4) enrichment has been presented in this study. Parametric study was conducted to investigate the effect of variation of process conditions for single stage without recycle (SSWR) and double stage with permeate recycle (DSPR) on product purity, CH4 recovery and compression power requirement. In the study, achieving high CH4 recovery and product purity simultaneously could not be attained in SSWR configuration. The performance of DSPR yielded a better result but with higher membrane area and compression power. DSPR configuration achieved high CH4 recovery and purity at increasing feed pressure, selectivity and feed flow. The CH4 losses increased in both configurations as %CO2 increased in the feed. DSPR configuration is considered the best configuration due to the end use of the product, as vehicular fuel, which requires high product purity

Operational study and simulation of a biogas upgrading plant

The drive for finding alternative energy to supplement fossil based fuel within the South African energy sector has led to research on waste to energy in particular biomethane as vehicular fuels. Biogas is produced from the anaerobic decomposition of organic matter with 40-70% vol. of methane. Biogas if upgraded, by removing the non-combustible component, can achieve 99% methane concentration which makes it a potent vehicle fuel and a direct substitute to natural gas. In this paper, a biogas upgrading plant operation that uses gas permeation technique for methane enrichment of biogas was studied and simulated. The effect of recycling permeate stream on methane recovery was studied. Recycling of the permeate stream improved the methane recovery of the simulated process by 18%. The overall methane recovery of the simulated process is 81.23%

Technology selection and siting of a biogas plant for OFMSW via multi-criteria decision analysis

Abstract: Multi-criteria decision analysis (MCDA) techniques were applied to choose a biogas digester technology and a site from a list of potential alternatives for an anaerobic digestion (AD) system utilising the organic fraction of municipal solid waste (OFMSW) based on a case study at the University of Johannesburg’s Doornfontein campus in South Africa. The simple multi-attribute rating technique (SMART) and analytic hierarchy process (AHP) techniques of MCDA were used to select a suitable biodigester model and site respectively. From a list of 14 biodigester technologies to be established at 1 of 3 potential sites in the study area, the most preferred model was the Puxin digester to be sited near the Aurum ladies’ residence within the school campus to supply biogas for heating purposes

Sizing of an anaerobic biodigester for the organic fraction of municipal solid waste

The anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) for biogas production is a potential solution to the growing challenges associated with municipal solid waste (MSW) management while simultaneously providing an alternative clean energy source. Biogas is produced by the anaerobic digestion (AD) of biomass using microorganisms in specifically designed plants called biogas digesters under controlled conditions or naturally in marshes and landfills. It is a rather clean and versatile fuel as opposed to fossil fuels. To design an efficient AD system, a proper understanding of the quality and quantity of available feedstock must be made as well as prevailing operating conditions. This paper represents steps that were taken to come up with an optimal size of biodigester to treat OFMSW produced at the University of Johannesburg’s Doornfontein Campus in downtown Johannesburg. The campus generates 232.2kg of OFMSW per day which required 30m3 of biodigester capacity

Technology selection of biogas digesters for OFMSW via multi-criteria decision analysis

Multi-criteria decision analysis (MCDA) techniques are becoming increasingly popular in decision making for technology selection because of their ability to capture the multi-dimensionality of technologies. Biogas typically refers to an odourless gas produced by anaerobic digestion of biomass using microorganisms. Its production can occur naturally in marshes and landfills or more commonly, in specifically designed plants called biogas digesters under controlled conditions. For techno-economic efficiency of a biodigester, several factors such as cost of plant are taken into consideration. This paper examines various available technologies for biogas digesters using defined selection criteria via MCDA and chooses the best alternatives at various scales of biogas production for a case study in South Africa with municipal biowaste as the target feedstock. 14 biogas plants were analysed in this study and the Puxin and Bio4gas digesters were the best alternatives for small and large scale biogas production respectively

Bio-methane potential of the organic fraction of municipal solid waste

Biogas is a gas formed from the breakdown of biomass by microorganisms in an anaerobic environment composed of methane (50%–70%) and carbon dioxide (30%–50%). The upgrading of biogas by the removal of carbon dioxide to increase the percentage of methane to over 92% produces bio-methane which is a potent versatile clean fuel. This paper represents a study that was carried out at the University of Johannesburg’s Doornfontein Campus (UJ DFC) to ascertain the potential of bio-methane recovery from the organic fraction of municipal solid waste (OFMSW) collected at the campus’ cafeteria and student residences. ..

Environmental sustainability : multi-criteria decision analysis for resource recovery from organic fraction of municipal solid waste

Abstract: Landfills within the City of Johannesburg (CoJ) are running out of airspace. To slow down airspace consumption rate, waste discharged at these landfills must be minimised, and where possible recover useful resources. A multi-criteria decision tool, the Analytical Hierarchy Process (AHP) was employed to appropriate technologies for fruit and vegetables waste discharge at Robinson Deep landfill. The goal of the approach is environmental sustainability. Pairwise comparison of four criteria and four technology alternatives were investigated. Data used were retrieved from a research group and consultations with waste to energy experts. Of the four technology alternatives, anaerobic digestion (AD) is the most preferred. Incineration technology has 49.42% preference to AD because it is perceived to reduce the bulkiness of waste discharged at the landfill. Composting has 25.24% preference to AD and it is believed to encourage home management of waste. Consistency ratio for all pairwise comparison was less than 0.1

A review on factors affecting municipal solid waste generation

Abstract: Municipal solid waste (MSW) management is not a one-off planning, it is a dynamic evolution and planning has to cater for it. The quantity of MSW generated and composition form the basis for planning and management of MSW. However, for an effective MSW reduction policy to be implemented, generated quantity of MSW is not sufficient alone for policy implementation but more of the variables affecting the generation rate and composition are critical. Without an in-depth understanding of these variables, waste reduction policies may be ineffective and unsuccessful. In this study, we reviewed the impact of these factors on MSW. A case of the City of Johannesburg (CoJ) was studied. Population and gross domestic product (GDP) are the two compelling factors affecting MSW generation. The waste generation per capita is influenced by income level. High income group generate on average 1.91 kg/capita/day, middle income group generates 1.01 kg/capita/day and low income group 0.92 kg/capita/day. This put the CoJ total waste generated at an average of 1.83 million ton/year

Municipal solid waste data quality on artificial neural network performance

Abstract: Short and long-term municipal solid waste (MSW) management requires adequate planning. Understanding the relationship among variables that affect MSW generation and predicting MSW based on them is needed for an effective planning. Methodologies to forecast MSW are numerous and have been implemented at different level of data granularity. Lack of data in many African cities and countries has hampered effective waste management plan. The lack of data has mainly been attributed to insufficient budget and lack of capacity to implement such management structure. In this study, we investigated the impact of data quality on forecasting efficiency using advanced prediction techniques. It was observed that the quality of waste related data variables determines the extent of model reliability and prediction accuracy