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The broad portfolio of negative emissions technologies calls for integrated analyses to explore the synergies between them and the power sector, with which they display strong links. These analyses should be conducted at a regional level, considering system uncertainties, assessing local benefits and the impact on carbon removal potential. This study investigates how uncertainty in electricity demand affects the optimal design of integrated carbon removal and power generation systems using multistage stochastic programming. Given the model complexity, we propose a tailored decomposition algorithm by extending previous work on the shrinking horizon approach that reduces the computational time by 90 %, enabling insights into various European scenarios. A combination of conventional technologies and biomass could satisfy the electricity demand while providing up to 9 Gt of net CO 2 removal from the atmosphere. Omitting uncertainties leads to an underestimation of the total cost and the selection of different technologies possibly leading to suboptimal performance. [ABSTRACT FROM AUTHOR]

• Applied Bayesian symbolic regression to hybrid modeling of process flowsheets. • Conducted deterministic global optimization of hybrid analytical surrogate models. • Global optimization of hybrid models outperforms that of black-box models. Modular chemical process simulators are widespread in chemical industries to design and optimize production processes with sufficient accuracy. However, convergence issues and entrapment in local optima during process optimization are still challenges to overcome. To circumvent them, surrogate models of first principles simulations have attracted attention as they are easier to handle, with hybrid surrogates combining data-driven surrogate models with mechanistic equations becoming particularly appealing. In this context, this work explores the use of Bayesian symbolic regression to construct and globally optimize hybrid analytical surrogate models of process flowsheets, where some units are approximated with tailored analytical expressions rather than with neural networks or Gaussian processes, which might be harder to globally optimize. Comparing with other prevalent black-box surrogate modeling & optimization approaches, such as kriging and Bayesian optimization, we find that our approach can find better solutions than those identified with pure black-box methodologies, yet model building is much more computationally demanding. [ABSTRACT FROM AUTHOR]