Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach

In this paper we provide a framework to cope with two problems: (i) the fragility of reinforcement learning due to modeling uncertainties because of the mismatch between controlled laboratory/simulation and real-world conditions and (ii) the prohibitive computational cost of stochastic optimal control. We approach both problems by using reinforcement learning to solve the stochastic dynamic programming equation. The resulting reinforcement learning controller is safe with respect to several types of constraints and it can actively learn about the modeling uncertainties. Unlike exploration and exploitation, probing and safety are employed automatically by the controller itself, resulting real-time learning. A simulation example demonstrates the efficacy of the proposed approach

State of the Art in Separation Processes for Alternative Working Fluids in Clean and Efficient Power Generation

Gas turbines must now comply with much stricter emission control regulations. In fact, to combat the greenhouse effect, regulatory authorities have drastically reduced allowable emission levels. For example, in less than 12 years, the United States' Clean Air Act issued the New Source Performance Standards (NSPS), which tightened the NOx emission margin of natural gas combustion (from 75 ppm to 10 ppm). On the other hand, despite those efforts, the high demand for energy produced by fossil-fueled gas turbines in power plants has resulted in dramatic increases in anthropogenic CO2 and NOx emitted by gas combustors. Most systems responsible for these undesirable emissions are directly linked to power generation, with gas turbines playing a pivotal role. Yet, gas turbines are still widely used in power plants and will continue to meet the growing demand. Therefore, sequestration and separation techniques such as Carbon Capture and Storage (CCS) and Air Separation Units (ASU) are essential to reduce CO2 and NOx emissions while allowing large amounts of power to be generated from these systems. This paper provides an in-depth exami-nation of the current state of the art in alternative working fluids utilized in the power generation industry (i.e., gas turbines, combustion). In addition, this paper highlights the recent contribution of integrating separation techniques, such as air separation, steam methane reforming, and water-gas shifting, to the power generation industry to facilitate a continuous and adequate supply of alternative working fluids.Funding: This publication was made possible by NPRP 13 grant # [NPRP13S-0203-200243] from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the author.Scopu