The biomass generated can be used as biofertilizers with slow-releasing nutrients properties.
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This Research Topic addresses this subject in three dedicated manuscripts. While low pH seemed to trigger the production of C2 and C3 compounds, values below 4. Consequently, the authors recommended to optimize the chain elongation process at a pH value of 4. The analysis of microbial dominance on complex heterogeneous mixed cultures of chain-elongating bacteria was conducted by Andersen et al.
Contrarily to the current view of medium chain fatty acids MCFA being uniformly inhibitory to fermentation, the authors demonstrated that the varied tolerance to MCFA within the community can lead to the dominance of some species and the suppression of others, which can result in a decreased productivity of the fermentation.
This was evidenced by a strong correlation between the dominance of the species Clostridium sp. BS-1, which can perform chain elongation to produce octanol, and the suppression of other species, as Clostridium sp. CPB-6, which can produce hexanoic acid, and Lactobacillus spp. This, in turn, causes an overall decrease of the MCFA production. A direct application of chain elongation in a real case platform has been studied by Kucek et al. In that manuscript, the authors use wine lees, which consisted primarily of settled yeast cells and ethanol from wine fermentation, as substrate to conduct continuous production of MCFA, specifically n-caprylate and n-caproate.
The experimental strategy was based on direct continuous in-line extraction through a membrane contactor of the MCFA to increase the product recovery ratio and enhance the mass transfer. By improving the mass transfer, they achieved the highest n-caprylate-to-n-caproate product ratio reported of 1. This work also entails one of the first approach in extracting high value-added MCFA from organic waste with no external electron donor addition, and therefore implies a clear technological advance for further up-scaling.
The biodiesel industry may be a promising option to compete with crude oil if production costs can be decreased. Side products from biodiesel production are currently one of the drawbacks for the sustainability of the process. Glycerol is the main one, and can be converted into high value-added bioproducts by specialized fermentation. Roume et al. As accumulation of metabolites has been an impediment for the enhancement of the process in previous BES applications, the authors propose to extract in situ the organic acids.
This caused a positive impact on the 1,3-propanediol yield and allowed to recover propionate from glycerol fermentation, which may open up possibilities for the improvement of the economic and environmental viability of the biodiesel production. An example where the resource can be biomass rather than waste itself is presented by Gu et al. A very specific community can be viewed as a value-added product to be used in onsite applications where the metabolic capabilities of the developed communities can be leveraged.
Resource recovery from wastewater is entering in a phase of technology development and upscaling. Europe is currently leading this stage as there are a wide range of technologies and startups that are being specialized on specific and highly technic processes patent stage. It is expectable that another technological round will lead to full industrial deployment of the resource recovery from wastewater by biological technologies once the big companies on water management will enter the scenario.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Key questions during facilitated breakout sessions aimed to identify and define metrics which would assess the sustainability of novel technologies. Those questions included the following: a What will we be able to achieve with a common set of metrics? The workshop was professionally facilitated during these interactive breakout sessions to help address these questions.
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The larger workshop group was divided into smaller subgroups with a trained facilitator, notetaker, and rapporteur assigned to each subgroup. Various stakeholder groups were represented within each subgroup.enter site
Brainstorming activities that led to the identification of important metrics were discussed within each subgroup through a facilitated discussion. After addressing key questions in the smaller groups, the rapporteur compiled data on key evaluation metrics that were important to each subgroup. The rapporteur provided a summary of the key metrics to the larger group and electronic versions of each presentation were collected from the rapporteurs. Each presentation was then evaluated to identify reoccurring themes and key metrics common to the subgroups.
Key metrics were grouped into common themes, and subsequently, four categories emerged from the analysis of performance metrics: environmental, economic, technical, and social sustainability.
The results and discussion highlight key metrics for resource recovery systems in the test bed network. Environmental performance metrics are generally used to quantify the intensity of environmental impacts caused by resource consumption and waste discharge associated with treatment processes. Additionally, environmental performance metrics can be used to measure the resource recovery potential of WRRFs. Environmental impacts of concern may vary with region, because the goals or interests of stakeholder groups will inevitably vary within the FAST Water Network.
As a result, a more fundamental approach that focuses on defining treatment performance inventory data as environmental performance metrics is adopted in this study. This is important because changes or upgrades in the technologies used at a specific point in the treatment train may affect the performance of downstream processes and thus the entire facility. The environmental performance inventory data collected allow for the examination of factors relevant to treatment, water reuse, energy recovery, and nutrient recycling from biosolids.
Inventory data can also be collected on different forms of nitrogen e. Similarly, different forms of carbon e.
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Although certain constituents are excluded from the currently proposed environmental metrics e. Project initiation and construction costs include not only capital expenditures for treatment infrastructure and equipment, but also area requirements e. Clearly, energy requirements also represent a significant operational cost; therefore, the energy data collected as an environmental performance indicator can be converted into energy cost in analyses of economic performance. Decommissioning costs that can potentially be estimated based on test bed evaluations include the labor and costs associated with dismantling or selling used equipment, any demolition work that is required, repurposing materials e.
Economic data are often subject to uncertainty because of a wide range of factors, and this uncertainty must be considered when assessing the performance of innovative technologies. For example, seasonal variations in water usage and shifts in population can lead to temporal and diurnal variability in energy and chemical usage. Additionally, variations in equipment life expectancy, labor, and training needs can affect operating and decommissioning costs.
Unfortunately, these costs may not be easily quantified at a test bed facility. The level of technological development and scale of implementation e. Furthermore, ongoing research and development could lead to technological improvements and reductions in production costs over time. Uncertainty or sensitivity analysis can be used to evaluate the influence of the aforementioned dynamic changes in WRRF performances.
Preserving and reusing resources
Although capital and operating equipment costs are critical economic performance metrics, this information is often proprietary and the least accessible to stakeholders. The FAST Water Network is exploring ways to store sensitive cost information that can be used by stakeholders interested in comparing the economic performance of different technologies, while simultaneously protecting the proprietary information of vendors and entrepreneurs who may need to limit the dissemination of this information during commercial development phases.
Protocols for storing, sharing, and accessing sensitive cost data must be designed so that innovation is encouraged, not hindered. Another concern is that capital costs are not scalable. Technical performance metrics can be used to inform stakeholders regarding the a resilience, b scalability, and c ease of integration of emerging technologies. As noted above, diurnal and seasonal variations in wastewater flows and properties affect chemical usage and energy requirements and, thus, operating costs.
Moreover, by collecting data on the performance of treatment technologies as a function of input variability throughout the test bed network, stakeholders will be able to assess their applicability under a wide range of climate conditions. Although testing at multiple scales can sometimes lead to conflicting results, testing and piloting can also lead to a greater understanding of operational strategies, in which further optimization is needed once the system is running at full scale. Prudent engineering judgment and experience can be used to develop efficient testing strategies and protocols, to facilitate transitions from bench scale to full scale.
The process optimization that can occur when technologies are tested at multiple scales can also lead to improved ease of integration for novel technologies. Ease of integration also depends on the type of treatment process biological, chemical, or physical treatment , because biological processes are often more sensitive to changes in operation, compared with chemical and physical processes.
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Another factor that affects ease of integration is the level of automation. Increased automation can increase operational reliability, but may require greater operator skill and training. As technologies are scaled up, the ability to operate and control them improves, as noted above. Although the majority of these societal indicators cannot be measured in individual test beds, there are some social performance metrics that can be measured within the test bed network and used by stakeholders to evaluate the social sustainability of innovative technologies.
These include the potential to create a nuisances such as odors, noise, dust, excessive truck traffic, or undesirable aesthetics, b spills or other hazards that could negatively affect the health of utility workers or the surrounding community, c jobs or opportunities for employee development, and d institutional effects, such as implications on data management, risk management, policies, and incentives. Additional data that could be used to assess a technology's adherence to a community value system e. These data will provide the foundation for performing additional evaluations on the environmental, economic, and social performance of innovative WRRFs.
The growing global demand for nutrients, coupled with the rising costs of natural gas consumed in ammonia production and the depletion of phosphorus reserves, indicates that the potential for nutrient recovery will become increasingly important to stakeholders. Similarly, the potential to achieve water reclamation to reduce potable water usage and recovery of energy from waste streams to replace nonrenewable energy sources will be of interest to local government officials and other stakeholders who are interested in the economic and environmental benefits of innovative treatment technologies.
The environmental performance and economic metrics described herein form the basis of these inventories.
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LCA based on these process inventories can thus be used to quantify the environmental impacts and benefits of WRRFs, including resource consumption, embodied energy, eutrophication potential, carbon footprint and greenhouse gas emissions, and other effects of global concern e. Different stakeholders will likely value environmental benefits and impacts differently. Hence, a weighting scheme can be developed e.
This is an encouraging trend because adoption of asset management practices is critical to the development of more sustainable water infrastructure. Most importantly, asset management allows these stakeholders to provide desired services while minimizing the total life cycle costs associated with owning and operating capital assets.
EPA, The potential for resource recovery using an innovative technology and other environmental performance metrics will determine what service levels can be achieved and sustained if the technology is adopted when existing equipment or infrastructure is replaced or upgraded as part of an asset management plan. Whether or not these services are actually desired by water infrastructure end users and other stakeholders can be assessed based on the value proposition of resource recovery to stakeholders, customer pain points, as well as the performance of the technology based on social metrics.
The identification of critical assets is an important component of asset management because this information can be used to identify vulnerable processes and equipment, predict the effect of asset failure on the performance and services provided by a utility, and develop plans for responding to and mitigating the effects of these failures U. Technical performance metrics monitored in the test bed network, particularly data that can be used to characterize the resiliency of new technologies, and environmental performance data will be crucial to the identification of critical assets and appropriate responses to failure.
In particular, the need to identify critical assets highlights the value of using the test bed network to subject new treatment technologies to standardized performance stress tests. Furthermore, the evaluation of maintenance practices and the likelihood of human adoption of these are critical in the definition of a reasonably accurate LCCA. Thus, an extended evaluation of novel technologies in different settings e. The economic performance metrics collected during the testing of a technology in the test bed network—project initiation and construction, operation, and decommissioning costs—will provide the foundational information needed to estimate its life cycle costs.
Water infrastructure faces numerous challenges created by the growing global human population, increasing demand for potable water, and dwindling nonrenewable resources. The FAST Water Network is expected to provide a space for innovation and experimentation in nascent treatment technologies at the bench scale, pilot scale, and full scale. The proposed metrics discussed in this article are broad enough that they can be used by a wide range of stakeholders in the network to conduct the analyses needed to make decisions that lead to the development of a more sustainable water infrastructure.
Importantly, just as best practices must be continuously evaluated and improved upon, the test bed metrics identified here should be revisited routinely and modified as stakeholder needs evolve. Standardization of test data will establish the legitimacy of the data, enable objective comparison of technologies, simplify data interpretation, and facilitate dissemination of information to stakeholders. This work was supported, in part, by the National Science Foundation under Grant Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of National Science Foundation, Department of Energy, Environmental Protection Agency, or Water Research Foundation.
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