Modelamiento matematico - UNAJ
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This document discusses modeling mathematical approaches for designing preventative measures in food industry quality management systems. It introduces variables related to processes like time, concentration, temperature and action that impact quality. It also discusses validation methods, both virtual and physical, that can be used along with sensors and modeling to develop predictive models of processes. Examples of online resources and spreadsheet applications are provided to illustrate modeling concentration and time profiles for chemical disinfection processes.
Modelamiento matematico - UNAJ
- 1. MODULO III INOCUIDAD Y DISEÑO DE PROCESOS EN LA INDUSTRIA ALIMENTARIA MODELAMIENTO MATEMATICO PARA EL DISEÑO DE MEDIDAS PREVENTIVAS EN SISTEMAS DE GESTION DE CALIDAD MSc. JOSE MANUEL PRIETO [email protected] [email protected] [email protected] https://www.youtube.Jose Manuel Prieto (com/channel/UCEFgEiJq8DhQRSPo4Oi7owg?view_as=subscriber)
- 7. VIRTUAL FISICA SENSORES QUIMIOMETRIA ATP 3M ISOPADOS CULTIVOS ESPACIO MODELO VARIABLES DE OPERACIÓN: DEL PROCESO, RESPUESTA, DEPENDIENTES, INDEPENDIENTES, CONSTANTES FISICAS, CONSTANTES CINETICAS, CONSTANTES QUIMICAS ECUACION DE GOBIERNO
- 9. RECURSOS EN LINEA GRATUITOS DE SOLUCIONES DE MODELOS PREDICTIVOS https://people.umass.edu/~aew2000/WolframDemoLinks.html https://www.combase.cc/index.php/en/ http://www.rpaulsingh.com/learning/virtual/experiments/pred _microbiology/index.html
- 11. APLICACIONES EN HOJAS DE CALCULO ChemicalDisinfectionData.xls ChemicalDisinfection.xls 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35 40 Concentration(ppm) Time (min) Concentration vs Time Profile -6 -5 -4 -3 -2 -1 0 0 5 10 15 20 25 30 35 40 LogSurvival Time (min) Log Survival vs Time ChemicalDisinfection.xlsTimeInit nPts Time Conc(Time) b(Conc(Time)) LS(Time) EqTime(Time) Background 0 41 0 32 3.336219259 -0.01 0.000299541 How to run 1 28.9547974 2.899824736 -2.993374659 0.97440845 Modifications dt EqnRoot 2 26.1993841 2.514290092 -4.645745169 1.817661913 References 1 -4.31066E-08 3 23.7061831 2.176350642 -5.82721258 2.506659505 4 21.4502415 1.882633514 -6.719111284 3.067801255 TimeFinal LSCurr 5 19.4089811 1.629530117 -7.410096041 3.524746873 40 -11.17298377 6 17.5619724 1.413175697 -7.954596068 3.897694391 7 15.8907297 1.229528694 -8.389124261 4.203118247 LSInit LSPrev 8 14.3785269 1.074514402 -8.739912455 4.454575217 -0.01 -11.14318842 9 13.0102291 0.94418617 -9.026317812 4.663043548 10 11.7721421 0.834864578 -9.262839335 4.837305641 Chart Settings: n bSum 11 10.6518747 0.743232451 -9.460434826 4.984324613 TimeAxisMin 0.705 0.437894858 12 9.63821478 0.666381368 -9.627437935 5.109588608 0 13 8.72101738 0.601817123 -9.770220848 5.217409762 TimeAxisMax k bCurr 14 7.89110285 0.547436531 -9.893682352 5.311173929 40 0.15 0.218038375 15 7.14016512 0.501487796 -10.00161042 5.39354332 TimeAxisTitle 16 6.46068858 0.4625241 -10.09695261 5.466617498 Time (min) ConcCrit bPrev 17 5.84587277 0.429356852 -10.18201801 5.532059381 ConcAxisMin 10 0.219856484 18 5.28956442 0.401012365 -10.25862843 5.591192914 0 19 4.78619582 0.376693689 -10.32823174 5.645078324 ConcAxisMax ConcInit ConcCurr 20 4.33072906 0.355748114 -10.39198715 5.694569956 35 32 0.586100444 21 3.9186057 0.337640105 -10.45082961 5.7403607 ConcAxisTitle 22 3.54570107 0.321929104 -10.5055187 5.783016174 Concentration (ppm) DisipExpRate ConcPrev 23 3.208283 0.308251494 -10.55667589 5.823001098 ConcMainTitle 0.1 0.647741166 24 2.90297451 0.296306053 -10.60481314 5.860699726 Concentration vs Time Profile 25 2.62671996 0.285842242 -10.65035496 5.896431734 Solve Clear