Resource recovery strategies for municipal water planning and management: a system dynamics modeling approach
Bibliographic record
Abstract
The true value of water has not yet been captured in municipal water planning and management, despite the increasing availability of technologies designed to create value from wastewater. Adapting system processes for water reclamation, energy generation, and production of other outputs (such as fertilizers) can transition wastewater treatment facilities into economically viable resource recovery facilities. Although resource recovery is an appropriate solution for municipal water management, its success depends mainly upon techno-economic feasibility, environmental sustainability, and public acceptance. Both the public and private sector face several barriers to advancing resource recovery, such as operational challenges, inappropriate regulatory frameworks, and a lack of capacity to develop or evaluate business plans pertaining to resource recovery and water reuse. A variety of possible solutions can be defined based on the principle of resource recovery, such as decentralized treatment and reuse, co-digestion with organic waste for energy, and nutrient recovery through struvite precipitation. A broad range of products can potentially be generated from wastewater at different time scales into the future. The purpose of this study is to represent and analyze the value of products that impact the feasibility of both established and emerging resource recovery strategies for municipal water planning and management. A decision support tool developed using system dynamics modeling software represents the various system components that are important to decision makers. The dynamic simulation model permits testing of the economic feasibility of specific resource recovery strategies and facilitates planning of resilient water systems. The system dynamics model represents real world processes through nonlinear feedbacks, changing variables, and delays – and is intended to increase understanding of the effects of feedbacks between resource recovery sub-systems with external drivers and variables. The model presented runs at a weekly time step and simulates the performance of user-specified resource recovery strategies into the future under various population growth, climate change, and water consumption/conservation scenarios, revealing trade-offs in the medium- to long-term. Simulating potential future scenarios by altering important input parameters helps to identify the most important variables to the economics of the specific strategies considered. The model is applied to the City of Calgary, Alberta to explore the impact to the City’s overall per capita water demand, based on the theory of diffusion of innovations framework and population served by source-separated resource recovery systems. The model was calibrated with financial data from existing utilities that have successfully integrated resource recovery strategies, in some cases recovering the entire costs of the capital investment required to upgrade in less than 3 years. This thesis includes a description of model characteristics and capabilities, as well as results that highlight the value of system dynamics modelling methods, which simulate system behaviour and shed light on cause-and-effect relationships, representing resource recovery systems in a realistic, comprehensive way. Decentralized wastewater systems with greywater reuse could help the City achieve its goal of reducing per capita water use below 350 liters with either the “intermediate” or “fast” adoption rates. The final result for the average per capita daily municipal water demand at the end of 2043 was 302, 348, and 391 liters per capita per day (Lpcd) for each of the three strategies tested (“fast”, “intermediate”, and “slow” rates of adoption). Retrofitted stormwater systems can also offset water production (up to 25 Lpcd of savings).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".