A hybrid mechanistic machine learning approach to model industrial network dynamics for sustainable design of emerging carbon capture and utilization technologies

Abstract

Industrial networks consist of multiple industrial nodes interacting with each other through material exchanges that support the overall production goal of the network. These industrial networks exhibit complex nonlinear dynamics arising due to the multiscale nature of interactions among industries and the inherent dynamics of each industrial node. Furthermore, these overall dynamics have a significant impact on the sustainable design of these networks, along with the resource consumption and emission dynamics of the overall network. However, understanding the overall dynamics of industrial networks is challenging as digital models do not exist for the whole network dynamics, especially for emerging industrial systems, and simulative analyses of the same can be computationally expensive. To overcome this limitation, we propose a hybrid mechanistic machine learning approach based on data-driven system identification to build surrogate dynamic models of industrial nodes, which can be coupled to evaluate the overall industrial network dynamics. Furthermore, we propose utilizing the overall network dynamics to quantify the dynamic carbon footprint and design of industrial networks for a maximum carbon sink. We apply our methodology to evaluate the dynamic carbon footprint of an algal-biodiesel industrial network comprising 5 separate dynamic industrial systems. The redesign of the network with the modified technological parameters informed by overall network dynamics results in an approximately 2% enhanced CO₂ sequestration rate of 29 750.34 kg h⁻¹, with the net CO₂ footprint being accurately calculated as −1485069.47 kg for 50 hours of operation based on the nonlinear model obtained for the network. The dynamic models were also used to analyze the net neutralization time required to completely remove the energy-related CO₂ emissions using this specific algal biodiesel network for a specific region in a particular year, providing insights into the potential of this technology to meet the climate mitigation goals. Hence, the proposed approach establishes a pathway to evaluate industrial network dynamics for any emerging system by relying on mechanistic models and data-driven system identification and informing the sustainable design of future industrial networks.