. This report summarizes results of a project to character& air toxic emissions of selected hazardous air pollutants (HAPS) from the AirPol Gas Suspension Absorption SO2 control technology. This project was sponsored by AirPol Inc., the Tennessee Valley Authority (TVA), and the U.S. Department of Energy (DOE). The GSA process utilizes a semi-dry lime scrubber for SO2 and HCI removal. Most of th...

EXECUTIVE SUMMARY This document serves as a DOE post-project assessment of a project in Round 3 of the Clean Coal Technology (CCT) Demonstration Program titled, "Demonstration of SO 2 Removal Using Gas Suspension Absorption Technology." In October 1990, AirPol Inc. entered into a cooperative agreement to conduct the study, with the Tennessee Valley Authority (TVA) as the host and co-sponsor. Th...

Gravitational search algorithm (GSA) is based on the law of gravity and a mass of interaction. In GSA, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. This paper proposes a new clustered gravitational search algorithm (CGSA) to accelerate the performance of the GSA. Here, the whole population is divided into th...

In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimizer of a single sample’s loss function, and takes average strategy to calculate reasonable learning rate sequence. While most existing gradient-based...

GSA; Real power loss; Reactive power planning; FACTS; Optimization Abstract In this paper, Gravitational Search Algorithm (GSA) is used as optimization method in reactive power planning using FACTS (Flexible AC transmission system) devices. The planning problem is formulated as a single objective optimization problem where the real power loss and bus voltage deviations are minimized under diffe...

In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by...

Gravitational Search Algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problems, the process of grouping data into clusters such that the data in each c...