(718b) Climate-Resilient Process Design Using the Flexibility Analysis Approach

The impacts of climate change can be fatal in various ways. We have seen the increased fluctuation of precipitation and temperature, which causes frequent natural disasters, such as flooding, drought, severe storms, and hurricanes. These are the direct impacts of climate change primarily related to the water cycle, which is one of the supporting services from ecosystems. The change in supporting ecosystem services will affect other ecosystem services (i.e., provisioning, regulating, and cultural services) as well as economic and societal activities that rely on ecosystem services. Numerous studies indicated that the knock-on (indirect) impacts of climate change on economic activities are significant as well. For example, Martinich and Crimmins (2019) estimated that the overall economic damages in the U.S. range in the hundreds of billions of dollars annually by 2100 under a high emission scenario. However, mitigation strategies under a low emission scenario will result in substantial economic benefits. Therefore, it is critical that we need to investigate both vulnerability and adaptation of technological systems to climate change.

Currently, most work on the climate change impacts has been conducted on agricultural and energy sectors. However, studies on manufacturing systems are still lacking. Designing flexible/resilient manufacturing systems to climate change is necessary to make them adapt to uncertain climate disturbances and changes in ecosystems and economic markets. Additional costs will be involved with adaptation strategies by showing trade-offs with the benefits of avoiding climate change impacts. In this work, we address climate-resilient process design (CRPD) problems using a flexibility analysis (FA) approach.

The main motivation for FA is to guarantee the operability (the capability of feasible operation) of manufacturing processes under changes and disturbances (uncertain parameters) at the design stage of processes [2]. Uncertain parameters can be physical properties such as inlet temperature, flow rate, and concentrations; and economic properties such as price and demand of products. The flexibility of a given design can be calculated by maximizing the range of unknown parameters that ensure a feasible operation. Also, by including a cost minimization function and solving a multi-objective optimization problem, optimal designs with desired flexibility and cost can be identified.

FA is suited for exploring CRPD since future climate projections are uncertain. Numerous global climate models (GCMs) have been developed to project future climate data. Based on the climate data, future ecosystem provisioning services, such as water supply, can be simulated using hydrological modeling or ecosystem modeling tools. For example, water is an important resource for any manufacturing system since it is used for cooling and steam-heating processes. Therefore, those projected and simulated data can be considered as uncertain parameters for the FA study. The preliminary study on a heat exchanger network (HEN) shows that its flexibility needs to be increased to adapt it to future climate disturbances (warmer water temperature). Also, the study exhibits the increased investment cost as a trade-off with higher flexibility.

Although FA has not yet been applied to investigate the flexibility of manufacturing systems to the climate change impacts, there are apparent research opportunities to explore adaptation strategies using FA. FA methodology has been established for decades to address flexible process design problems. Therefore, applying FA for CRPD could be readily acceptable and applicable to engineering disciplines. Moreover, since FA is based on the mathematical engineering model, it is extendable for integrating with other mathematical models such as supply chain models and economic models. Thus, the flexibility of processes can be examined while accounting for supply chain networks and the planetary boundary. Considering that climate change has indirect impacts on many sectors, incorporating a broad system boundary in the model could be particularly valuable. However, the increased computational challenges will be a trade-off.

References

[1] Martinich, J., & Crimmins, A. (2019). Climate damages and adaptation potential across diverse sectors of the United States. Nature climate change, 9(5), 397-404.

[2] Grossmann, I. E., Calfa, B. A., & Garcia-Herreros, P. (2014). Evolution of concepts and models for quantifying resiliency and flexibility of chemical processes. Computers & chemical engineering, 70, 22-34.