(147q) Optimization-Based Strategies for Decarbonization of Energy Systems

The chemical sector is currently the largest industrial consumer of oil and gas and a major emitter of greenhouse gases. Since a significant portion of greenhouse gas (GHG) emissions is from the combustion of fossil fuels to meet the energy requirements and the production of feedstocks, it is indispensable to decarbonize energy infrastructure and chemical production.

This decarbonization may require significant modifications in the current energy supply approaches, as well as the method to deal with unavoidable emissions. Herein, the primary choices for transitioning away from fossil fuels and lowering carbon emissions include: (a) improving energy efficiency; (b) electrifying fossil fuel-based energy supply; and (c) capturing, storing, and utilizing CO₂. However, considering the intermittency of these variable renewable sources and high energy requirement of carbon capture, the system-level techniques involving process design, operation, and control, are necessary to ensure flexible, safe, and cost-effective operations of decarbonized energy systems.

1.1 Carbon Capture, Utilization, and Storage (CCUS) for Shale Gas Production System

Shale gas is natural gas trapped in unconventional reservoirs and is currently an important source of natural gas in the United States. Since a large portion of electricity is still generated by natural gas combustion, there is a need to use CCUS techniques to mitigate the associated CO₂ emissions for decarbonization purposes. To this end, my group will at first focus on developing a holistic optimization framework for the integration of shale gas supply chain, water supply chain (i.e., hydraulic fracturing water cycle), and CCUS supply chain (i.e., CO₂ generation from power plant, CO₂ capture route, transportation, storage, and utilization). This integration should be reasonable considerating that the captured CO₂ can be particularly utilized and injected into shale wells to increase production rates, and the depleted and/or even active shale reservoirs are promising sites for geologic carbon sequestration by direct CO₂ injection. Besides, to determine the optimal design of the CCUS supply chain network, my group will also focus on reservoir simulation to model the CO₂ sequestration process, which can dynamically determine the capacity and durability of the shale reservoirs taken as storages. Finally, capacity planning will also be discussed regarding the entire shale gas production system to investigate the feasibility of modular manufacturing.

1.2. Sustainable Energy Systems Design and Optimization

The design of energy systems is increasingly focusing on systems penetrated by renewable energies. In particular, electrification options of Power-to-Chemical and Power-to-Heat using renewable energy gives the opportunity to produce chemical products and supply heat in a green manner with net zero carbon emissions. To this end, my group will at first focus on physically-based modeling of electrified unit operations, such as electrochemical reactors for ammonia production, and electricity heating, such as microwave heating. These models will be studied individually and then integrated into the traditional chemical production process and heat integration approach to comprehensively evaluate the feasibility of electrification. Considering the intermittency of solar and wind sources, the implementation of energy storage systems and/or backup fossil power generation is also necessary to fill the time periods when no renewable energy is available. Therefore, in addition to process design, my group will also focus on demand response scheduling, decision-making under uncertainty, and control strategy for the safe and profitable operation of the energy systems.

1.3 Multiscale Modeling and Optimization of Supply Chains

Optimization problems have many applications in chemical and system engineering, such as process operations involving planning, scheduling, real-time optimization, and control, which have significantly different temporal and spatial scales, as well as responsibilities. Considering the interconnected and multi-scale nature (i.e., unit operation level, process level, supply chain level) of the energy systems (i.e., shale gas production system and sustainable energy system aforementioned), it is necessary to simultaneously consider the multiple decisions layers to avoid suboptimal or even infeasible solutions that could be obtained through traditional hierarchical/sequential approaches. However, despite the apparent benefits of simultaneous approaches, the large size of simultaneous optimization models can be problematic. To this end, my group will at first focus on the two approaches for model formulation: (a) Top-down approach: surrogate model representing the lower-level decision layer is developed and then integrated into the higher-level model; and (b) bottom-up approach: economic considerations in the higher-level decision layer are incorporated into the process control system (i.e., as economic model predictive control (EMPC)). Then, data-driven surrogate modeling, decomposition algorithms, rolling horizon approaches, and other techniques will be investigated to handle the complexity of the resulting model. Finally, two more challenges will be focused on afterward: (a) uncertainty from different decision layers must also be simultaneously considered; and (b) simultaneous models must be solved repeatedly in real time to manipulate operation strategies according to feedback information from the control level.

2. Research Experience

During my Ph.D., I have been involved in the Texas A&M Energy Institute and Artie McFerrin Department of Chemical Engineering at Texas A&M University. Under the supervision of Prof. Joseph Kwon, my research work is mainly about the integration of pumping profile design and water management optimization for shale gas production systems. To this end, we have developed: (a) an economic model-based controller design framework for hydraulic fracturing [1]; (b) a holistic optimization model for shale gas supply chain network (SGSCN) incorporating hydraulic fracturing water cycle, where capacity planning for conventional treatment facility and modular device is highlighted [2,3]; and (c) an optimization framework for the closed-loop integration of scheduling and offset-free model predictive control (MPC) of hydraulic fracturing [4].

As for my postdoctoral research at Purdue University, I got the chance to work with Prof. Can Li focusing on multiscale optimization, electrified energy systems, and mixed-integer nonlinear programming (MINLP) algorithms. Specifically, we propose to adapt parametric cost function approximations (CFAs) to handle the computational challenge in the integrated planning, scheduling, and control problem [P1]. On the other hand, we develop an optimization framework to integrate the design and operation of heat exchanger network, renewable-based power generating units, power-to-heat (PtH) units, and energy storage units, to improve the overall energy efficiency [P2]. These two studies have been submitted to the current AIChE Annual Meeting and will be presented if being accepted.

Presentations submitted to the current AIChE Annual Meeting

[P1] Cao K, Ramanujam A, Li C. A Parametric Cost Function Approximation Algorithm for Multiscale Decision-Making.

[P2] Cao K, Li C. Heat Integration of Renewable Electricity-Driven Power-to-Heat Technologies Under Time-Varying Electricity Price.

References

[1] Cao K, Siddhamshetty P, Ahn Y, Mukherjee R, Kwon JS. Economic model-based controller design framework for hydraulic fracturing to optimize shale gas production and water usage. Ind Eng Chem Res 2019;58.

[2] Cao K, Siddhamshetty P, Ahn Y, El-Hawagi M, Kwon JS. Evaluating the spatiotemporal variability of water recovery ratios of shale gas wells and their effects on shale gas development. J Clean Prod 2020;276:123171.

[3] Cao K, Sitapure N, Kwon JS. Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management. J Clean Prod 2023;384:135282.

[4] Cao K, Son SH, Moon J, Kwon JS. A closed-loop integration of scheduling and control for hydraulic fracturing using offset-free model predictive control. Appl Energy 2021;302:117487.