Sustainable Energy and Process Systems

Achieving a sustainable future requires the integration of a portfolio of technologies and the coordination of multiple sectors including energy, chemicals, agriculture, and transportation. Here, systems engineering plays a key role as it enables us to identify synergies and trade-offs that arise from these complex interactions. In our research, we aim to optimize the design and operation of sustainable process systems across multiple spatio-temporal scales, from process to supply chain and from scheduling of plant operations at the hourly level to long-term planning over many years.

Electrification is increasingly being recognized as a key strategy for decarbonizing the chemical industry. In our group, we work on the optimal design and operation of renewables-based electrified process systems, assessing their techno-economic viability and providing insights into what is needed to accelerate their development and deployment. One good example is our research on green ammonia, which can be produced using hydrogen from renewables-powered water electrolysis. Here, we address not only the production of green ammonia but also its utilization as the world's main fertilizer precursor and as a versatile energy medium.

A key aspect in modeling renewables-based systems is to capture the impact of intermittent renewable energy sources such as solar and wind. As an example, the figure below shows the operational schedule of an offshore wind-powered green ammonia plant, where we see highly dynamic operation over the course of a representative day as well as seasonal changes over the course of a year. When designing a chemical plant under such conditions, we cannot apply the conventional practice of assuming a constant nominal production rate. Instead, we must account for detailed dynamic operations, which often leads to highly complex optimization models involving large numbers of decision variables and constraints. Here, we leverage our expertise in surrogate modeling and decomposition-based solution algorithms to significantly reduce the computation times.

The power grid is designed to reliably match electricity supply and demand. This task has become increasingly challenging due to high fluctuations in electricity demand and increasing penetration of intermittent renewable energy into the electricity supply mix. Demand side management (DSM), which refers to the active management of electricity consumption, leverages the flexibility on the demand side to respond to varying grid conditions and has proven to be a very effective means to improving grid efficiency and reliability.

The industrial sector is particularly well suited for DSM due to the large sizes of individual industrial loads and the high level of flexibility in many manufacturing processes. In our research, we take the perspective of power-intensive chemical processes and investigate the benefits from DSM, which is especially relevant in the context of industrial electrification. If properly executed, it will help power-intensive industries achieve significant cost savings as well as improve grid reliability without adding more power generation capacities. Our work addresses multiple important but challenging aspects of industrial DSM: (1) accurate modeling of operational flexibility, (2) integration of production and energy management, (3) decision-making across multiple temporal and spatial scales, (4) optimization under uncertainty, and (5) coordination across multiple stakeholders.

Carbon monetization generally refers to the use of financial incentives to encourage the reduction of CO₂ emissions; the most common mechanisms include imposing a carbon tax, emissions trading, and carbon credit markets. Especially carbon credits have received significant attention in recent years as they offer a business case for companies developing emissions abatement technologies. However, they may not offer the best solution for the chemical industry as the prices offered in a carbon credit market may not reflect the actual cost of reducing the carbon footprint of a chemical product. Therefore, chemical companies are starting to establish their own carbon accounting systems to enable the certification and sale of low-carbon products to customers willing to pay a premium. This also presents tremendous opportunities for systems analysis, e.g. for low-carbon product portfolio selection, investment planning, contracting and pricing, risk analysis, and cross-functional integration.

• Kong, B., Daoutidis, P., & Zhang, Q. (2026). Design of microgrids with ammonia-based energy storage via Bayesian optimization. Energy Conversion and Management, 359, 121529. 
• Kankani, K., Palys, M. J., Daoutidis, P., & Zhang, Q. (2026). Assessing the techno-economic impact of process flexibility on methanol- and syngas-based e-kerosene production. Industrial & Engineering Chemistry Research, 65(11), 6077-6091.
• Jeong, Y. & Zhang, Q. (2025). Transition toward a decarbonized steel industry: A supply chain analysis for the United States. Industrial & Engineering Chemistry Research, 64(33), 16263-16280.
• Rathi, T., Brzakala, C., Wang, H., & Zhang, Q. (2025). Assessing the impact of chain of custody models on the long-term planning of low-carbon ammonia supply chains. Industrial & Engineering Chemistry Research, 64(3), 1680-1699.
• Jagana, J. S., Amaran, S., & Zhang, Q. (2025). Multistage robust mixed-integer optimization for industrial demand response with interruptible load. Computers & Chemical Engineering, 194, 108974.
• Andrés-Martínez, O., Malmali, M., Zhang, Q., & Daoutidis, P. (2024). Optimal design of an absorbent-enhanced ammonia synthesis process for solar thermochemical energy storage. ACS Sustainable Chemistry & Engineering, 12(25), 9446-9460.
• Kong, B., Zhang, Q., & Daoutidis, P. (2024). Nonlinear model predictive control of flexible ammonia production. Control Engineering Practice, 148, 105946.
• Kong, B., Zhang, Q., & Daoutidis, P. (2024). Real-time operation of a stand-alone microgrid with green ammonia storage. IEEE Transactions on Control Systems Technology, 32(4), 1463-1470.
• Riley, B. P., Daoutidis, P., & Zhang, Q. (2023). Multi-scenario design of ammonia-based energy storage systems for use as non-wires alternatives. Journal of Energy Storage, 73, 108795.
• Wang, H., Daoutidis, P., & Zhang, Q. (2023). Ammonia-based green corridors for sustainable maritime transportation. Digital Chemical Engineering, 6, 100082.
• Allman, A. & Zhang, Q. (2022). Distributed fairness-guided optimization for coordinated demand response in multi-stakeholder process networks. Computers & Chemical Engineering, 161, 107777.
• Wang, H., Daoutidis, P., & Zhang, Q. (2021). Harnessing the wind power of the ocean with green offshore ammonia. ACS Sustainable Chemistry & Engineering, 9, 14605-14617.
• Allman, A., Lee, C., Martín, M., & Zhang, Q. (2021). Biomass waste-to-energy supply chain optimization with mobile production modules. Computers & Chemical Engineering, 150, 107326.
• Wang, H., Palys, M. J., Daoutidis, P., & Zhang, Q. (2021). Optimal design of sustainable ammonia-based food-energy-water systems with nitrogen management. ACS Sustainable Chemistry & Engineering, 9, 2816-2834.
• Allman, A. & Zhang, Q. (2020). Distributed cooperative industrial demand response. Journal of Process Control, 86, 81-93.
• Flores-Quiroz, A., Pinto, J. M., & Zhang, Q. (2019). A column generation approach to multiscale capacity planning for power-intensive process networks. Optimization & Engineering, 20(4), 1001-1027.
• Zhang, Q. & Grossmann, I. E. (2016). Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives. Chemical Engineering Research & Design, 116, 114-131.