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.