Dynamic optimization of the mashing process
This work aims to demonstrate the applicability of dynamic optimization to improve the time-temperature schedule of a brewery mashing process, based on kinetic models available in the literature. The mashing process consists in the enzymatic degradation of the polysaccharides present in the malt. Th...
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Veröffentlicht in: | Food control 2009-12, Vol.20 (12), p.1127-1140 |
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description | This work aims to demonstrate the applicability of dynamic optimization to improve the time-temperature schedule of a brewery mashing process, based on kinetic models available in the literature. The mashing process consists in the enzymatic degradation of the polysaccharides present in the malt. This is a fundamental step within the brewery activity since the composition of the mashing wort determines the quality of the final product. The main reactions that take place in the mashing are the degradation of starch, β-glucans and arabinoxylans into small chain fermentable and non-fermentable carbohydrates. The manipulation of the temperature profile of the batch reactor is the main mechanism to control the extent of the ongoing reactions. Since high temperatures favor the production of fermentable matter but also increases the concentration of undesirable species in the wort, the choice of an adequate temperature profile is not obvious. Dynamic optimization studies with a complete mashing model demonstrate that profiles of “temperature averages” of about 51
°C are preferred over typical industrial mashings of 64
°C to optimize the operation. |
doi_str_mv | 10.1016/j.foodcont.2009.03.004 |
format | Article |
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°C are preferred over typical industrial mashings of 64
°C to optimize the operation.</description><subject>brewing industry</subject><subject>Dynamic optimization</subject><subject>dynamic testing</subject><subject>enzymatic hydrolysis</subject><subject>enzyme activity</subject><subject>food processing</subject><subject>food processing quality</subject><subject>kinetics</subject><subject>mashing</subject><subject>Mashing process</subject><subject>model food systems</subject><issn>0956-7135</issn><issn>1873-7129</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkMlOwzAQhi0EEqXwCpATF5QwXuI4N1BZpUocoGfL8dK6auISp0jl6XEVOHOaOXz_LB9ClxgKDJjfrgsXgtGhGwoCUBdACwB2hCZYVDSvMKmP0QTqkqeelqfoLMY1AK4AwwTdPOw71Xqdhe3gW_-tBh-6LLhsWNmsVXHlu2W27YO2MZ6jE6c20V781ilaPD1-zF7y-dvz6-x-nmvKqyHnjpeCC8NIaSrXOKbqEiqOtdFMNI02wgpjGlfqktTEWacpS5c5A1bjShA6Rdfj3LT3c2fjIFsftd1sVGfDLkoCnFLKDiAfQd2HGHvr5Lb3rer3EoM8uJFr-edGHtxIoDK5ScGrMehUkGrZ-ygX7wQwTRnGoBaJuBsJmx798raXUXvbaWt8b_UgTfD_LfkBSxl6Jg</recordid><startdate>20091201</startdate><enddate>20091201</enddate><creator>Durand, G.A.</creator><creator>Corazza, M.L.</creator><creator>Blanco, A.M.</creator><creator>Corazza, F.C.</creator><general>Elsevier Ltd</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20091201</creationdate><title>Dynamic optimization of the mashing process</title><author>Durand, G.A. ; Corazza, M.L. ; Blanco, A.M. ; Corazza, F.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-6f65868d425d7fbf4a950761cdc48bbcd8e8ddbf5c5292fefc34713fd0ec17823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>brewing industry</topic><topic>Dynamic optimization</topic><topic>dynamic testing</topic><topic>enzymatic hydrolysis</topic><topic>enzyme activity</topic><topic>food processing</topic><topic>food processing quality</topic><topic>kinetics</topic><topic>mashing</topic><topic>Mashing process</topic><topic>model food systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Durand, G.A.</creatorcontrib><creatorcontrib>Corazza, M.L.</creatorcontrib><creatorcontrib>Blanco, A.M.</creatorcontrib><creatorcontrib>Corazza, F.C.</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Food control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Durand, G.A.</au><au>Corazza, M.L.</au><au>Blanco, A.M.</au><au>Corazza, F.C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic optimization of the mashing process</atitle><jtitle>Food control</jtitle><date>2009-12-01</date><risdate>2009</risdate><volume>20</volume><issue>12</issue><spage>1127</spage><epage>1140</epage><pages>1127-1140</pages><issn>0956-7135</issn><eissn>1873-7129</eissn><abstract>This work aims to demonstrate the applicability of dynamic optimization to improve the time-temperature schedule of a brewery mashing process, based on kinetic models available in the literature. The mashing process consists in the enzymatic degradation of the polysaccharides present in the malt. This is a fundamental step within the brewery activity since the composition of the mashing wort determines the quality of the final product. The main reactions that take place in the mashing are the degradation of starch, β-glucans and arabinoxylans into small chain fermentable and non-fermentable carbohydrates. The manipulation of the temperature profile of the batch reactor is the main mechanism to control the extent of the ongoing reactions. Since high temperatures favor the production of fermentable matter but also increases the concentration of undesirable species in the wort, the choice of an adequate temperature profile is not obvious. Dynamic optimization studies with a complete mashing model demonstrate that profiles of “temperature averages” of about 51
°C are preferred over typical industrial mashings of 64
°C to optimize the operation.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.foodcont.2009.03.004</doi><tpages>14</tpages></addata></record> |
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subjects | brewing industry Dynamic optimization dynamic testing enzymatic hydrolysis enzyme activity food processing food processing quality kinetics mashing Mashing process model food systems |
title | Dynamic optimization of the mashing process |
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