The increasing trend of world-wide energy consumption emphasizes the importance of ongoing optimization of new and existing technologies. In this dissertation, two energy–intensive systems are simulated and optimized. Advanced estimation, optimization, and control techniques such as a moving horizon estimator and a model predictive controller are developed to enhance the profitability, product quality, and reliability of the systems. An enabling development is presented for the solution of complex dynamic optimization problems. The strategy involves an initialization approach to large–scale system models that both enhance the computational performance as well as the ability of the solver to converge to an optimal solution. One particular application of this approach is the modeling and optimization of a batch distillation column. For estimation of unknown parameters, an L1-norm method is utilized that is less sensitive to outliers than a squared error objective. The results obtained from the simple model match the experimental data and model prediction for a more rigorous model. A nonlinear statistical analysis and a sensitivity analysis are also implemented to verify the reliability of the estimated parameters. The reduced–order model developed for the batch distillation column is computationally fast and reasonably accurate and is applicable for real time control and online optimization purposes. Similar to estimation, an L1-norm objective function is applied for optimization of the column operation. Application of an L1-norm permits explicit prioritization of the multi–objective problems and adds only linear terms to the problem. Dynamic optimization of the column results in a 14% increase in the methanol product obtained from the column with 99% purity. In a second application of the methodology, the results obtained from optimization of the hybrid system of a cryogenic carbon capture (CCC) and power generation units are presented. Cryogenic carbon capture is a novel technology for CO2 removal from power generation units and has superior features such as low energy consumption, large–scale energy storage, and fast response to fluctuations in electricity demand. Grid–level energy storage of the CCC process enables 100% utilization of renewable power sources while 99% of the CO2 produced from fossil–fueled power plants is captured. In addition, energy demand of the CCC process is effectively managed by deploying the energy storage capability of this process. By exploiting time–of–day pricing, the profit obtained from dynamic optimization of this hybrid energy system offsets a significant fraction of the cost of construction of the cryogenic carbon capture plant.

Cryogenic carbon capture removes CO2 and other pollutants from flue and waste stream gases produced from the combustion of fossil fuels such as coal, natural gas, and oil and the production of cement. A transient, 1-dimensional numerical model was developed to study the temperature profile within a counter-current surface CO2 desublimation-falling liquid or solid heat exchanger. Effects of desublimation heat and mass transfer as well as convective and conductive heat transfer relationships were taken into account. Experiments show that CO2 can be captured on a falling spherical particle when appropriate column operating conditions are met.

Global warming is becoming ever increasing concern in our society. As such the likelihood of a carbon tax in the US is becoming increasingly likely. A carbon tax will be expensive enough that coal-based power plants will either have to install carbon capture technology or close. The two front runner technologies for carbon capture are amine scrubbing, and oxyfuel combustion. The downside is that both of these technologies increase power generation cost in a new plant by about 80% and have up to a 30% parasitic load, which reduces the cycle efficiency, that is, the power production per unit fuel consumed, by the same 30%. Retrofitting existing plants by either of these technologies is even more expensive and inefficient since it requires major modifications or replacement of the existing plant in addition to the new capture technology. Sustainable Energy Solutions (SES) has developed a carbon capture technology named cryogenic carbon capture (CCC). CCC is a process by which the flue gas cools to the point that CO2 desublimates. This process is more efficient, cheaper, and has about half of the parasitic load of other technologies, approaching the theoretical minimum in CO2 separation within heat exchanger and compressor efficiencies. This thesis conceptually describes, experimentally characterizes, and theoretically models one desublimating heat exchanger as an integral part of the CCC process. A spray tower conceptually developed by SES and theoretically and experimentally explored in previous work at lab scale is developed at bench scale in this work with accompanying major modifications to the theoretical model. It sprays a cold contact liquid to cool warm gas (relative to the contact liquid) that travels up the tower. Nominal operating temperatures are around -120 to -130 °C for 90% and 99% capture, respectively. Once the flue gas cools enough, CO2 desublimates on the liquid droplet surfaces and forms a slurry with the contact liquid. This spray tower can achieve arbitrarily high CO2 capture efficiency, depending on the temperature of the exiting gas and other operational variables. The experimental data outlined here varied these operational parameters over broad ranges to achieve capture efficiencies of 55% to greater than 95%, providing a robust data set for model comparison. The operational parameters explored include liquid temperature, liquid flow rate, gas flow rate, and droplet size. These data validated a transport and design model that predicts capture for future scale-up and design of the project. The data and model indicate expected behaviors with most of these variables and a dependence on internal droplet temperature profiles that may be higher than expected. This project significantly advanced the experimental database and the model capabilities that describe the spray tower.

The cryogenic carbon capture™ (CCC) process provides energy- and cost-efficient carbon capture and can be configured to provide an energy storage system using an open-loop natural gas (NG) refrigeration system, which is called energy storing cryogenic carbon capture (CCC-ES™). This investigation focuses on the transient operation and especially on the dynamic response of this energy storage system and explores its efficiency, effectiveness, design, and operation. This investigation included four tasks.The first task explores the steady-state design of four different natural gas liquefaction processes simulated by Aspen HYSYS. These processes differ from traditional LNG process in that the CCC process vaporizes the LNG and the cold vapors return through the LNG heat exchangers, exchanging sensible heat with the incoming flows. The comparisons include costs and energy performance with individually optimized processes, each operating at three operating conditions: energy storage, energy recovery, and balanced operation. The second task examines steady-state and transient models and optimization of natural gas liquefaction using Aspen HYSYS. Steady-state exergy and heat exchanger efficiency analyses characterize the performance of several potential systems. Transient analyses of the optimal steady-state model produced most of the results discussed here. The third task explores transient Aspen HYSYS modeling and optimization of two natural gas liquefaction processes and identifies the rate-limiting process components during load variations. Novel flowrate variations included in this investigation drive transient responses of all units, especially compressors and heat exchangers. Model-predictive controls (MPC) effectively manages such heat exchangers and compares favorably with results using traditional controls. The last task shows how an unprocessed natural gas (NG) pretreatment system can remove more than 90% of the CO2 from NG with CCC technology using Aspen Plus simulations and experimental data. This task shows how CCC-based technology can treat NG streams to prepare them for LNG use. Data from an experimental bench-scale apparatus verify simulation results. Simulated results on carbon (CO2) capture qualitatively and quantitatively agree with experimental results as a function of feedstock properties.

Global climate change concerns help shape current environmental regulations, which increasingly seek to reduce or capture CO2 emissions. Methods for capturing CO2 emissions from energy processes have been the focus of numerous studies to provide support for those seeking to reduce the environmental impact of their processes. This research has (1) simulated a baseline case of energy-storing cryogenic carbon capture for implementation on a 550 MWe coal fired power plant, (2) presented a novel cryogenic carbon capture process for removing CO2 from natural gas down to arbitrary levels, (3) presented a natural gas liquefaction process that has the ability to be highly CO2 tolerant, and (4) developed theoretical models and their experimental validation of CO2 capture predictions for all aforementioned processes.