![Optimizing the specific surface area of fly ash-based sorbents for flue gas desulfurization [An article from: Chemosphere]](https://www.ebooknetworking.net/books/B00/0RR/bigB000RR5ITO.jpg)
Optimizing the specific surface area of fly ash-based sorbents for flue gas desulfurization [An article from: Chemosphere]
Book Details
PublisherElsevier
ISBN / ASINB000RR5ITO
ISBN-13978B000RR5IT7
AvailabilityAvailable for download now
Sales Rank10,988,465
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Chemosphere, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32h, 5-15g, and 0-12g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m^2g^-^1 could be obtained by mixing 13.1g of coal fly ash and 5.5g of calcium sulfate in a hydration process containing 100ml of water and 5g of calcium oxide for a fixed hydration time of 10h.
Description:
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32h, 5-15g, and 0-12g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m^2g^-^1 could be obtained by mixing 13.1g of coal fly ash and 5.5g of calcium sulfate in a hydration process containing 100ml of water and 5g of calcium oxide for a fixed hydration time of 10h.
