Mathias Riveros-Gomez, Daniela Zalazar-García, Iside Mut, Rodrigo Torres-Sciancalepore, María Paula Fabani, Rosa Rodriguez and Germán Mazza*,
{"title":"从昆斯生物废料中可持续提取果胶的生物精炼生产方案的多目标优化与实施","authors":"Mathias Riveros-Gomez, Daniela Zalazar-García, Iside Mut, Rodrigo Torres-Sciancalepore, María Paula Fabani, Rosa Rodriguez and Germán Mazza*, ","doi":"10.1021/acsengineeringau.2c00018","DOIUrl":null,"url":null,"abstract":"<p >The objective of this study was to optimize the pectin extraction from industrial quince biowaste using citric acid as a hydrolytic agent and assisting the process with ultrasound technology. For this, the process was modeled using the Box–Behnken design (BBD) to find the factors’ optimum values and their interactions. The quince pectin extraction was carried out by adding to the biowaste a citric acid solution at different pH values (2.0, 2.5, and 3.0) in mass volume ratios of 1/25, 1/20, and 1/15 g/mL and immersing it in an ultrasound bath for 30, 45, and 60 min at controlled temperatures of 70, 80, and 90 °C. Pectin yield, process cost, and CO<sub>2</sub> emission were calculated under different conditions according to the BBD model, and a polynomial function was adjusted for each dependent variable. A multiobjective optimization technique known as “Genetic algorithms” was used to find the proper extraction conditions that would maximize the pectin yield and minimize the process cost. The optimal extraction conditions obtained were as follows: pH = 2.12, mvr = 0.04 g/mL, time = 48.98 min, and temperature = 85.20 °C, with response variables of pectin yield = 12.78%, cost = 1.501 USD/kg of pectin, and calculated CO<sub>2</sub> emission = 0.565 kg of CO<sub>2</sub>/kg of pectin.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"2 6","pages":"496–506"},"PeriodicalIF":4.3000,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.2c00018","citationCount":"5","resultStr":"{\"title\":\"Multiobjective Optimization and Implementation of a Biorefinery Production Scheme for Sustainable Extraction of Pectin from Quince Biowaste\",\"authors\":\"Mathias Riveros-Gomez, Daniela Zalazar-García, Iside Mut, Rodrigo Torres-Sciancalepore, María Paula Fabani, Rosa Rodriguez and Germán Mazza*, \",\"doi\":\"10.1021/acsengineeringau.2c00018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >The objective of this study was to optimize the pectin extraction from industrial quince biowaste using citric acid as a hydrolytic agent and assisting the process with ultrasound technology. For this, the process was modeled using the Box–Behnken design (BBD) to find the factors’ optimum values and their interactions. The quince pectin extraction was carried out by adding to the biowaste a citric acid solution at different pH values (2.0, 2.5, and 3.0) in mass volume ratios of 1/25, 1/20, and 1/15 g/mL and immersing it in an ultrasound bath for 30, 45, and 60 min at controlled temperatures of 70, 80, and 90 °C. Pectin yield, process cost, and CO<sub>2</sub> emission were calculated under different conditions according to the BBD model, and a polynomial function was adjusted for each dependent variable. A multiobjective optimization technique known as “Genetic algorithms” was used to find the proper extraction conditions that would maximize the pectin yield and minimize the process cost. The optimal extraction conditions obtained were as follows: pH = 2.12, mvr = 0.04 g/mL, time = 48.98 min, and temperature = 85.20 °C, with response variables of pectin yield = 12.78%, cost = 1.501 USD/kg of pectin, and calculated CO<sub>2</sub> emission = 0.565 kg of CO<sub>2</sub>/kg of pectin.</p>\",\"PeriodicalId\":29804,\"journal\":{\"name\":\"ACS Engineering Au\",\"volume\":\"2 6\",\"pages\":\"496–506\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2022-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.2c00018\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Engineering Au\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsengineeringau.2c00018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.2c00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Multiobjective Optimization and Implementation of a Biorefinery Production Scheme for Sustainable Extraction of Pectin from Quince Biowaste
The objective of this study was to optimize the pectin extraction from industrial quince biowaste using citric acid as a hydrolytic agent and assisting the process with ultrasound technology. For this, the process was modeled using the Box–Behnken design (BBD) to find the factors’ optimum values and their interactions. The quince pectin extraction was carried out by adding to the biowaste a citric acid solution at different pH values (2.0, 2.5, and 3.0) in mass volume ratios of 1/25, 1/20, and 1/15 g/mL and immersing it in an ultrasound bath for 30, 45, and 60 min at controlled temperatures of 70, 80, and 90 °C. Pectin yield, process cost, and CO2 emission were calculated under different conditions according to the BBD model, and a polynomial function was adjusted for each dependent variable. A multiobjective optimization technique known as “Genetic algorithms” was used to find the proper extraction conditions that would maximize the pectin yield and minimize the process cost. The optimal extraction conditions obtained were as follows: pH = 2.12, mvr = 0.04 g/mL, time = 48.98 min, and temperature = 85.20 °C, with response variables of pectin yield = 12.78%, cost = 1.501 USD/kg of pectin, and calculated CO2 emission = 0.565 kg of CO2/kg of pectin.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)