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OS.1. Example: Studying alternatives of food waste recyclingEfficient treatment and energy recovery from organic waste such as food waste is one of the important sustainability challenges faced by many modern societies. Two main technologies deployed to handle organic waste are incineration (waste-to-energy) and AD. Incineration is a mature technology that converts general combustible waste into useful heat and power. AD is an emerging treatment technique that uses micro-organisms to break down food waste and produces biogas and digestate in the meanwhile. The biogas can then be harnessed to fuel gas engines for power generation. The remaining digestate is further converted into bio-compost. The evaluation and comparison of environmental impacts is a mandatory requirement in any feasibility study of deploying these waste treatment options on a large scale. For example, Khoo (2009) evaluates the environmental performances of various solid waste treatments in Singapore to guide the development of future waste management strategies. However, one practical problem is that data from different reference sources can be inconsistent or lacking when performing the input calibration of life cycle models, especially for the relatively new and undeveloped technologies.In this example, we apply the proposed impact risk valuation model to compare the above two waste treatment techniques. Three environmental impact categories are considered: GWP, AP and photochemical oxidation potential (PCOP). We use data inputs primarily motivated by the citystate of Singapore where the majority of general waste is directly disposed via incineration. In recent years, however, the local environmental authorities have in place high priority studies of deploying

We consider a design problem for wastewater treatment systems that considers uncertainty in pollutant concentration levels at water sources. The goal is to optimize the selection of treatment technologies and pipeline connections, so that treated wastewater can achieve specified effluents discharge limits as well as possible. We propose a new two‐stage model to optimize a set of guarantee levels, that is, the maximum concentration level of source pollutants for which treated wastewater can be compliant with discharge limits. In the first stage, treatment technologies and pipeline connections are selected. In the second stage, when pollutant concentration levels are revealed, wastewater distribution and mixing are determined. A key attractiveness of the proposed guarantee rate optimization model is that it can be simplified into a single‐stage mixed‐integer linear program. In our numerical experiments based on real‐world pollutants data, the guarantee rate model demonstrates its advantages in terms of computational efficiency, scalability and solution quality, compared with the standard probability maximization model. Finally, the methodology proposed in this paper can also be applied to other two‐stage problems under uncertainty with similar uncertainty characteristics.

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