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Award ID contains: 2313289

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  1. ; (, Reviews in Chemical Engineering)
    Abstract Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control. 
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    Free, publicly-accessible full text available December 10, 2025
  2. ; ; ; (, ACS Materials Letters)
    Free, publicly-accessible full text available June 2, 2026
  3. ; ; ; ; (, Computers & Chemical Engineering)
    Free, publicly-accessible full text available June 1, 2026
  4. ; (, Industrial & Engineering Chemistry Research)
    Free, publicly-accessible full text available April 9, 2026
  5. ; ; (, PSE Press)
    This paper considers the optimal incorporation of renewable ammonia production facilities into existing supply chain networks which import ammonia from conventional producers while accounting for uncertainty in this conventional ammonia price. We model the supply chain transition problem as a two-stage stochastic optimization problem which is formulated as a Mixed Integer Linear Programming problem. We apply the proposed approach to a case study on Minnesota's ammonia supply chain. We find that accounting for conventional price uncertainty leads to earlier incorporation of in-state renewable production sites in the supply chain network and a reduction in the quantity and cost of conventional ammonia imported over the supply chain transition horizon. These results show that local renewable ammonia production can act as a hedge against the volatility of the conventional ammonia market. 
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  6. ; ; (, Industrial & Engineering Chemistry Research)
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  7. ; (, Computers & Chemical Engineering)
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  8. ; (, Computers & Chemical Engineering)
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  9. ; (, Journal of Process Control)
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  10. ; ; (, Processes)
    This work considers the incorporation of renewable ammonia manufacturing sites into existing ammonia supply chain networks while accounting for ammonia price uncertainty from existing producers. We propose a two-stage stochastic programming approach to determine the optimal investment decisions such that the ammonia demand is satisfied and the net present cost is minimized. We apply the proposed approach to a case study considering deploying in-state renewable ammonia manufacturing in Minnesota’s supply chain network. We find that accounting for price uncertainty leads to supply chains with more ammonia demand met via renewable production and thus lower costs from importing ammonia from existing producers. These results show that the in-state renewable production of ammonia can act as a hedge against the volatility of the conventional ammonia market. 
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