Development of Advanced Composting Technologies for \nMunicipal Organic Waste Treatment in Small Communities \nin Newfoundland and Labrador
Bibliographic record
Abstract
Municipal Solid Waste (MSW) is one of the major fractions of the solid waste in Canada. From \n2002 to 2008, Canadian municipal solid waste disposal has increased from 769 kilograms to 777 \nkilograms per capita. Among the provinces, Newfoundland and Labrador (NL) has one of the \nhighest waste disposal levels per capita in the country. According to the Multi Materials \nStewardship Board (MMSB), it is estimated that more than 400,000 tonnes of municipal solid \nwaste (MSW) materials are generated each year in this province and organic waste makes up as \nmuch as 30% of all waste generated. To properly manage MSW generated, the Provincial Solid \nWaste Management Strategy has been identified in 2002, aiming to reduce the amount of waste \ngoing into landfills by 50 per cent. \nComposting has been regarded as an efficient and effective way to deal with the organic waste \nand helps work toward achieving the provincial 50 per cent waste reduction goal. It also creates \nrich organic soil that can enhance lawns and gardens. Therefore, MSW composting has been \nlisted as one of the six new environmental standards applied to new waste management systems \nin NL. However, NL comprises more than 200 small communities without access to the central \ncomposting facility. For those areas, small-scale composting technologies are desired to manage \ntheir MSW so as to reduce collection and transport costs and eliminate the other environmental \ncontamination during transportation. \nComposting is a biological process that is affected by chemical and physical factors. The lack of \nunderstanding of the complexity of biological, chemical, and physical processes can result in \nmalfunction of a composting system. The microbial and physicochemical environment in \ncomposting can be affected by the diversity of microbial population, temperature, bulking agent, \naeration, and chemical properties of raw material such as the C/N ratio and moisture content. \nInteractions among biological, chemical, and physical factors are crucial to the comprehensive \nunderstanding of the composting process, and thus viable for process control and system \noptimization. \nThis project aims at developing composting technologies applicable to northern communities in \nNL, and conducting system optimization to increase the composting efficiency and improve \ncompost quality. Six composting reactors (50×20×25 cm) were designed and manufactured. Six \nmixers were installed in each reactor. An inlet was designed to provide air through a vacuum \npump. A perforated plate with holes was installed for air distribution in the system. The exhaust \ngas was monitored by a gas monitoring system and then discharged into a flask containing \nH2SO4 solution (1 M) to absorb the NH3. To prevent heat loss, heat insulating layers were \ndesigned and applied to cover the reactor thoroughly. Reactors were filled with food waste as \nraw material. Factorial design was applied, with sixteen runs conducted, to optimize the \noperational factors including moisture content, aeration, bulking agent, and C/N ratio. Each \ncomposting run lasts 30 days. The effect of main factors and their interactions on composting \nprocess was investigated by measuring temporal variations of enzyme activities (dehydrogenase, \nβ-glucosidase, and Phosphomonoesterase), germination index (GI), pH, electrical conductivity \n(EC), temperature, moisture, ash content, oxygen uptake rate (OUR), and C/N ratio during \ncomposting. \nExperimental results showed that the breakdown of organic matter by microbial activities led to \nincrease in the temperature of the composting material. As composting progresses, the amount of \ndegradable matter decreased and the temperature declined. When most of the organic matter was \nconsumed, the temperature in the reactor dropped to the ambient temperature. The OUR can \nexpress biological activities during composting and biological stability at the end of composting. \nThe OUR values showed strong correlation with temperature. The maximum OUR was observed \nconcurrently with the maximum temperature. The pH value was low at the first stage due to the \naccumulation of organic acids, and increased gradually while organic acids were consumed by \nmicroorganisms. The EC values increased in all runs as a result of cation concentration \nincrement. Moisture content showed descending trends in all runs due to the evaporation under \nhigh temperature. As a result of decomposition of organic matter by composting, the organic \nmatter decreased and ash content increased in all runs. Although the GI data showed notable \nfluctuation during composting, it started to increase at the end of the composting process. In \nmost of the runs, the peaks of dehydrogenase activity as an indicator of biological activity were \nobserved with the maximum temperature and OUR value simultaneously. The β-glucosidase \nactivity showed with high values at the themophilic phase and after the temperature drop. In \naddition, high activity of phosphomonoesterase accrued during the thermophilic phase. \nResults of the factorial design indicated that aeration rate, moisture content, and bulking agents \naffect the maximum temperature significantly. Aeration rate has significant influence on the \nmaximum OUR. The C/N ratio and the interaction between aeration rate and bulking agent have \nmajor impact on GI. Moisture content is an important factor affecting the cumulative \ndehydrogenase and the β-glucosidase activity. The C/N ratio influences the β-glucosidase \nactivity as well. The output of this research can help to design the small-scale composting \nsystem for MSW management in small communities in NL, and provide a solid base of technical \nand scientific knowledge for system operation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".