狠狠撸shows by User: CAChemE / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: CAChemE / Wed, 16 Jan 2019 06:18:25 GMT 狠狠撸Share feed for 狠狠撸shows by User: CAChemE Mixed-integer and Disjunctive Programming - Ignacio E. Grossmann /CAChemE/mixedinteger-and-disjunctive-programming-ignacio-e-grossmann shortcoursegbdminlp19alicante-190116061825
1. Introduction Mathematical Programming 2. Mixed-integer Linear Programming (MILP) 3. Propositional Logic and Disjunctions 4. Mixed-integer Nonlinear Programming (MINLP) 5. Generalized Disjunctive Programming (GDP) 6. Global optimization]]>
1. Introduction Mathematical Programming 2. Mixed-integer Linear Programming (MILP) 3. Propositional Logic and Disjunctions 4. Mixed-integer Nonlinear Programming (MINLP) 5. Generalized Disjunctive Programming (GDP) 6. Global optimization]]> Wed, 16 Jan 2019 06:18:25 GMT /CAChemE/mixedinteger-and-disjunctive-programming-ignacio-e-grossmann CAChemE@slideshare.net(CAChemE) Mixed-integer and Disjunctive Programming - Ignacio E. Grossmann CAChemE 1. Introduction Mathematical Programming 2. Mixed-integer Linear Programming (MILP) 3. Propositional Logic and Disjunctions 4. Mixed-integer Nonlinear Programming (MINLP) 5. Generalized Disjunctive Programming (GDP) 6. Global optimization <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shortcoursegbdminlp19alicante-190116061825-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 1. Introduction Mathematical Programming 2. Mixed-integer Linear Programming (MILP) 3. Propositional Logic and Disjunctions 4. Mixed-integer Nonlinear Programming (MINLP) 5. Generalized Disjunctive Programming (GDP) 6. Global optimization
Mixed-integer and Disjunctive Programming - Ignacio E. Grossmann from CAChemE
]]> 2889 1 https://cdn.slidesharecdn.com/ss_thumbnails/shortcoursegbdminlp19alicante-190116061825-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mixed-integer Models for Planning and Scheduling - Ignacio E. Grossmann /slideshow/mixedinteger-models-for-planning-and-scheduling-ignacio-e-grossmann/128142640 shortcourseps19alicante-190116061310
Goals 1. Gain an appreciation for optimization-based planning/scheduling 2. Learn general guidelines for modeling and solving these problems]]>
Goals 1. Gain an appreciation for optimization-based planning/scheduling 2. Learn general guidelines for modeling and solving these problems]]> Wed, 16 Jan 2019 06:13:10 GMT /slideshow/mixedinteger-models-for-planning-and-scheduling-ignacio-e-grossmann/128142640 CAChemE@slideshare.net(CAChemE) Mixed-integer Models for Planning and Scheduling - Ignacio E. Grossmann CAChemE Goals 1. Gain an appreciation for optimization-based planning/scheduling 2. Learn general guidelines for modeling and solving these problems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shortcourseps19alicante-190116061310-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Goals 1. Gain an appreciation for optimization-based planning/scheduling 2. Learn general guidelines for modeling and solving these problems
Mixed-integer Models for Planning and Scheduling - Ignacio E. Grossmann from CAChemE
]]> 3947 6 https://cdn.slidesharecdn.com/ss_thumbnails/shortcourseps19alicante-190116061310-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Simulation of Chemical Rectors - Introduction to chemical process simulators - Coco - DWSIM- Aspen - Hysys - free - course /slideshow/simulation-of-chemical-rectors-introduction-to-chemical-process-simulators-coco-dwsim-aspen-hysys-free-course/66948165 introductiontochemicalprocesssimulators-tutorialonsimulationofchemicalreactors-coco-dwsim-aspen-hysy-161010081251
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org ]]>
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org ]]> Mon, 10 Oct 2016 08:12:50 GMT /slideshow/simulation-of-chemical-rectors-introduction-to-chemical-process-simulators-coco-dwsim-aspen-hysys-free-course/66948165 CAChemE@slideshare.net(CAChemE) Simulation of Chemical Rectors - Introduction to chemical process simulators - Coco - DWSIM- Aspen - Hysys - free - course CAChemE Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontochemicalprocesssimulators-tutorialonsimulationofchemicalreactors-coco-dwsim-aspen-hysy-161010081251-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org
Simulation of Chemical Rectors - Introduction to chemical process simulators - Coco - DWSIM- Aspen - Hysys - free - course from CAChemE
]]> 9084 12 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontochemicalprocesssimulators-tutorialonsimulationofchemicalreactors-coco-dwsim-aspen-hysy-161010081251-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to free and open source Chemical Process Simulators - (DWSIM & COCO) /slideshow/introduction-to-free-and-open-source-chemical-process-simulators/66661540 introductiontochemicalprocesssimulators-tutorial-coco-dwsim-aspen-hysys-free-course-161003083202
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course ]]>
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course ]]> Mon, 03 Oct 2016 08:32:02 GMT /slideshow/introduction-to-free-and-open-source-chemical-process-simulators/66661540 CAChemE@slideshare.net(CAChemE) Introduction to free and open source Chemical Process Simulators - (DWSIM & COCO) CAChemE Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontochemicalprocesssimulators-tutorial-coco-dwsim-aspen-hysys-free-course-161003083202-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0. You can find more learning material for chemical engineers in http://CAChemE.org TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course
Introduction to free and open source Chemical Process Simulators - (DWSIM & COCO) from CAChemE
]]> 10113 10 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontochemicalprocesssimulators-tutorial-coco-dwsim-aspen-hysys-free-course-161003083202-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Optimizacion con Python (Pyomo vs GAMS vs AMPL) https://es.slideshare.net/slideshow/optimizacion-con-python-pyomo-vs-gams-vs-ampl/55836243 optimizacion-python-pyomo-gams-ampl-151204215320-lva1-app6892
https://www.youtube.com/watch?v=LfBGGTUdbXU La optimizaci贸n o programaci贸n matem谩tica mediante lenguajes de modelado algebraico ---com煤nmente GAMS, AMPL y AIMMS--- es utilizada en la industria para la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos a la gesti贸n log铆stica de una empresa. Pyomo es un paquete de software de c贸digo abierto ---licenciado bajo BSD por Sandia National Laboratories, USA--- desarrollado en Python, y que soporta un conjunto diverso de capacidades de optimizaci贸n para la formulaci贸n y el an谩lisis de modelos de optimizaci贸n. En particular, Pyomo permite el modelado de problemas tipo LP, QP, NP, MILP, MINLP, MISP entre otros y se comunica con los principales solvers comerciales, gratuitos y/o libres, as铆 como la plataforma ofrecida por NEOS server. La resoluci贸n mediante m茅todos de optimizaci贸n ---comunes en un 谩mbito de investigaci贸n cient铆fica--- son a menudo desconocidos en la industria o bien delegados por falta de tiempo y/o recursos. Por tanto, su resoluci贸n acaba siendo mediante m茅todos menos eficientes que resultan en formas de trabajo con condiciones sustancialmente mejorables. Por este motivo, en esta charla, estudiantes de ingenier铆a qu铆mica de la Universidad de Alicante realizar谩n una introducci贸n visual a conceptos de optimizaci贸n, presentar谩n Pyomo y mostrar谩n la resoluci贸n de casos de estudio de diferentes industrias mediante este lenguaje de modelado algebraico desarrollado en Python.]]>
https://www.youtube.com/watch?v=LfBGGTUdbXU La optimizaci贸n o programaci贸n matem谩tica mediante lenguajes de modelado algebraico ---com煤nmente GAMS, AMPL y AIMMS--- es utilizada en la industria para la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos a la gesti贸n log铆stica de una empresa. Pyomo es un paquete de software de c贸digo abierto ---licenciado bajo BSD por Sandia National Laboratories, USA--- desarrollado en Python, y que soporta un conjunto diverso de capacidades de optimizaci贸n para la formulaci贸n y el an谩lisis de modelos de optimizaci贸n. En particular, Pyomo permite el modelado de problemas tipo LP, QP, NP, MILP, MINLP, MISP entre otros y se comunica con los principales solvers comerciales, gratuitos y/o libres, as铆 como la plataforma ofrecida por NEOS server. La resoluci贸n mediante m茅todos de optimizaci贸n ---comunes en un 谩mbito de investigaci贸n cient铆fica--- son a menudo desconocidos en la industria o bien delegados por falta de tiempo y/o recursos. Por tanto, su resoluci贸n acaba siendo mediante m茅todos menos eficientes que resultan en formas de trabajo con condiciones sustancialmente mejorables. Por este motivo, en esta charla, estudiantes de ingenier铆a qu铆mica de la Universidad de Alicante realizar谩n una introducci贸n visual a conceptos de optimizaci贸n, presentar谩n Pyomo y mostrar谩n la resoluci贸n de casos de estudio de diferentes industrias mediante este lenguaje de modelado algebraico desarrollado en Python.]]> Fri, 04 Dec 2015 21:53:20 GMT https://es.slideshare.net/slideshow/optimizacion-con-python-pyomo-vs-gams-vs-ampl/55836243 CAChemE@slideshare.net(CAChemE) Optimizacion con Python (Pyomo vs GAMS vs AMPL) CAChemE https://www.youtube.com/watch?v=LfBGGTUdbXU La optimizaci贸n o programaci贸n matem谩tica mediante lenguajes de modelado algebraico ---com煤nmente GAMS, AMPL y AIMMS--- es utilizada en la industria para la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos a la gesti贸n log铆stica de una empresa. Pyomo es un paquete de software de c贸digo abierto ---licenciado bajo BSD por Sandia National Laboratories, USA--- desarrollado en Python, y que soporta un conjunto diverso de capacidades de optimizaci贸n para la formulaci贸n y el an谩lisis de modelos de optimizaci贸n. En particular, Pyomo permite el modelado de problemas tipo LP, QP, NP, MILP, MINLP, MISP entre otros y se comunica con los principales solvers comerciales, gratuitos y/o libres, as铆 como la plataforma ofrecida por NEOS server. La resoluci贸n mediante m茅todos de optimizaci贸n ---comunes en un 谩mbito de investigaci贸n cient铆fica--- son a menudo desconocidos en la industria o bien delegados por falta de tiempo y/o recursos. Por tanto, su resoluci贸n acaba siendo mediante m茅todos menos eficientes que resultan en formas de trabajo con condiciones sustancialmente mejorables. Por este motivo, en esta charla, estudiantes de ingenier铆a qu铆mica de la Universidad de Alicante realizar谩n una introducci贸n visual a conceptos de optimizaci贸n, presentar谩n Pyomo y mostrar谩n la resoluci贸n de casos de estudio de diferentes industrias mediante este lenguaje de modelado algebraico desarrollado en Python. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimizacion-python-pyomo-gams-ampl-151204215320-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> https://www.youtube.com/watch?v=LfBGGTUdbXU La optimizaci贸n o programaci贸n matem谩tica mediante lenguajes de modelado algebraico ---com煤nmente GAMS, AMPL y AIMMS--- es utilizada en la industria para la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos a la gesti贸n log铆stica de una empresa. Pyomo es un paquete de software de c贸digo abierto ---licenciado bajo BSD por Sandia National Laboratories, USA--- desarrollado en Python, y que soporta un conjunto diverso de capacidades de optimizaci贸n para la formulaci贸n y el an谩lisis de modelos de optimizaci贸n. En particular, Pyomo permite el modelado de problemas tipo LP, QP, NP, MILP, MINLP, MISP entre otros y se comunica con los principales solvers comerciales, gratuitos y/o libres, as铆 como la plataforma ofrecida por NEOS server. La resoluci贸n mediante m茅todos de optimizaci贸n ---comunes en un 谩mbito de investigaci贸n cient铆fica--- son a menudo desconocidos en la industria o bien delegados por falta de tiempo y/o recursos. Por tanto, su resoluci贸n acaba siendo mediante m茅todos menos eficientes que resultan en formas de trabajo con condiciones sustancialmente mejorables. Por este motivo, en esta charla, estudiantes de ingenier铆a qu铆mica de la Universidad de Alicante realizar谩n una introducci贸n visual a conceptos de optimizaci贸n, presentar谩n Pyomo y mostrar谩n la resoluci贸n de casos de estudio de diferentes industrias mediante este lenguaje de modelado algebraico desarrollado en Python.
from CAChemE
]]> 17344 23 https://cdn.slidesharecdn.com/ss_thumbnails/optimizacion-python-pyomo-gams-ampl-151204215320-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Simulador de reactores qu铆micos - COCO Simulator - Free https://es.slideshare.net/slideshow/simulador-de-reactores-qumicos-coco-simulator/42825956 simuladordereactoresquimicoscocosimulator-141218040559-conversion-gate01
Quinta sesi贸n del curso de iniciaci贸n a la simulaci贸n de procesos qu铆micos con COCO Simulator y ChemSep COCO Simulator Simulaci贸n de reactores qu铆micos con COCO (CORN+COUSCUS) Reactor de conversi贸n fija Reactor de Flujo Pist贸n (RFP) con COCO Producci贸n de etilenglicol Reactor cont铆nuo de tanque agitado (RCTA) con COCO]]>
Quinta sesi贸n del curso de iniciaci贸n a la simulaci贸n de procesos qu铆micos con COCO Simulator y ChemSep COCO Simulator Simulaci贸n de reactores qu铆micos con COCO (CORN+COUSCUS) Reactor de conversi贸n fija Reactor de Flujo Pist贸n (RFP) con COCO Producci贸n de etilenglicol Reactor cont铆nuo de tanque agitado (RCTA) con COCO]]> Thu, 18 Dec 2014 04:05:59 GMT https://es.slideshare.net/slideshow/simulador-de-reactores-qumicos-coco-simulator/42825956 CAChemE@slideshare.net(CAChemE) Simulador de reactores qu铆micos - COCO Simulator - Free CAChemE Quinta sesi贸n del curso de iniciaci贸n a la simulaci贸n de procesos qu铆micos con COCO Simulator y ChemSep COCO Simulator Simulaci贸n de reactores qu铆micos con COCO (CORN+COUSCUS) Reactor de conversi贸n fija Reactor de Flujo Pist贸n (RFP) con COCO Producci贸n de etilenglicol Reactor cont铆nuo de tanque agitado (RCTA) con COCO <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/simuladordereactoresquimicoscocosimulator-141218040559-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Quinta sesi贸n del curso de iniciaci贸n a la simulaci贸n de procesos qu铆micos con COCO Simulator y ChemSep COCO Simulator Simulaci贸n de reactores qu铆micos con COCO (CORN+COUSCUS) Reactor de conversi贸n fija Reactor de Flujo Pist贸n (RFP) con COCO Producci贸n de etilenglicol Reactor cont铆nuo de tanque agitado (RCTA) con COCO
from CAChemE
]]> 11494 11 https://cdn.slidesharecdn.com/ss_thumbnails/simuladordereactoresquimicoscocosimulator-141218040559-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 S4 - Process/product optimization using design of experiments and response surface methodology - Session 4/4 /slideshow/session-4-processproductoptimizationdesignexperimentsresponsesurfacemethodolgysession4/42573452 s4-process-product-optimization-design-experiments-response-surface-methodolgy-session-4-141210113752-conversion-gate02
Session 3 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 Schedule and details: The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.]]>
Session 3 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 Schedule and details: The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.]]> Wed, 10 Dec 2014 11:37:52 GMT /slideshow/session-4-processproductoptimizationdesignexperimentsresponsesurfacemethodolgysession4/42573452 CAChemE@slideshare.net(CAChemE) S4 - Process/product optimization using design of experiments and response surface methodology - Session 4/4 CAChemE Session 3 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 Schedule and details: The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/s4-process-product-optimization-design-experiments-response-surface-methodolgy-session-4-141210113752-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Session 3 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 Schedule and details: The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.
S4 - Process/product optimization using design of experiments and response surface methodology - Session 4/4 from CAChemE
]]> 2208 3 https://cdn.slidesharecdn.com/ss_thumbnails/s4-process-product-optimization-design-experiments-response-surface-methodolgy-session-4-141210113752-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 S3 - Process product optimization design experiments response surface methodolgy - Session 3/4 /CAChemE/process-product-optimization-design-experiments-response-surface-methodolgy-session-3 processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session3-141207114958-conversion-gate02
Session 3/4 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.]]>
Session 3/4 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.]]> Sun, 07 Dec 2014 11:49:58 GMT /CAChemE/process-product-optimization-design-experiments-response-surface-methodolgy-session-3 CAChemE@slideshare.net(CAChemE) S3 - Process product optimization design experiments response surface methodolgy - Session 3/4 CAChemE Session 3/4 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session3-141207114958-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Session 3/4 鈥 Central composite designs, second order models, ANOVA, blocking, qualitative factors An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingenier铆a de Procesos Qu铆micos.
S3 - Process product optimization design experiments response surface methodolgy - Session 3/4 from CAChemE
]]> 2504 4 https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session3-141207114958-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 S2 - Process product optimization using design experiments and response surface methodolgy /slideshow/s2-process-product-optimization-design-experiments-response-surface-methodolgy-session-2/42236107 processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session2-141201164032-conversion-gate02
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥漖]>
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥漖]> Mon, 01 Dec 2014 16:40:32 GMT /slideshow/s2-process-product-optimization-design-experiments-response-surface-methodolgy-session-2/42236107 CAChemE@slideshare.net(CAChemE) S2 - Process product optimization using design experiments and response surface methodolgy CAChemE An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session2-141201164032-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥
S2 - Process product optimization using design experiments and response surface methodolgy from CAChemE
]]> 2733 6 https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session2-141201164032-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 S1 - Process product optimization using design experiments and response surface methodolgy /slideshow/s1-process-product-optimization-using-design-experiments-and-response-surface-methodolgy/42235983 processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session1-141201163626-conversion-gate02
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥漖]>
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥漖]> Mon, 01 Dec 2014 16:36:26 GMT /slideshow/s1-process-product-optimization-using-design-experiments-and-response-surface-methodolgy/42235983 CAChemE@slideshare.net(CAChemE) S1 - Process product optimization using design experiments and response surface methodolgy CAChemE An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session1-141201163626-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k鈮3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system. Mikko M盲kel盲 (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Ume氓, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.鈥
S1 - Process product optimization using design experiments and response surface methodolgy from CAChemE
]]> 5724 10 https://cdn.slidesharecdn.com/ss_thumbnails/processproductoptimizationdesignexperimentsresponsesurfacemethodolgy-session1-141201163626-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Python en ciencia e ingenieria: lecciones aprendidas https://es.slideshare.net/slideshow/python-en-ingenieria-lecciones-aprendidas/41347204 pythoneningenierialeccionesaprendidas-141110060143-conversion-gate02
驴Python cient铆fico? Este es un resumen de experiencias por parte de alumnos de ingenier铆a qu铆mica que empezaron con Python. 隆Python visto con los ojos de un novato! http://CAChemE.org]]>
驴Python cient铆fico? Este es un resumen de experiencias por parte de alumnos de ingenier铆a qu铆mica que empezaron con Python. 隆Python visto con los ojos de un novato! http://CAChemE.org]]> Mon, 10 Nov 2014 06:01:42 GMT https://es.slideshare.net/slideshow/python-en-ingenieria-lecciones-aprendidas/41347204 CAChemE@slideshare.net(CAChemE) Python en ciencia e ingenieria: lecciones aprendidas CAChemE 驴Python cient铆fico? Este es un resumen de experiencias por parte de alumnos de ingenier铆a qu铆mica que empezaron con Python. 隆Python visto con los ojos de un novato! http://CAChemE.org <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pythoneningenierialeccionesaprendidas-141110060143-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 驴Python cient铆fico? Este es un resumen de experiencias por parte de alumnos de ingenier铆a qu铆mica que empezaron con Python. 隆Python visto con los ojos de un novato! http://CAChemE.org
from CAChemE
]]> 1193 5 https://cdn.slidesharecdn.com/ss_thumbnails/pythoneningenierialeccionesaprendidas-141110060143-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Simulaci贸n de columnas de destilaci贸n multicomponente con COCO+ChemSep (alternativa freware a Aspen/Hysys) https://es.slideshare.net/slideshow/simulacin-de-columnas-de-destilacin-multicomponente-con-cocochemsep-alternativa-a-aspenhysys/39961178 simulacion-columnas-destilacion-multicomponente-coco-chemsep-aspen-141007041940-conversion-gate02
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]>
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]> Tue, 07 Oct 2014 04:19:40 GMT https://es.slideshare.net/slideshow/simulacin-de-columnas-de-destilacin-multicomponente-con-cocochemsep-alternativa-a-aspenhysys/39961178 CAChemE@slideshare.net(CAChemE) Simulaci贸n de columnas de destilaci贸n multicomponente con COCO+ChemSep (alternativa freware a Aspen/Hysys) CAChemE COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/simulacion-columnas-destilacion-multicomponente-coco-chemsep-aspen-141007041940-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.
from CAChemE
]]> 8128 5 https://cdn.slidesharecdn.com/ss_thumbnails/simulacion-columnas-destilacion-multicomponente-coco-chemsep-aspen-141007041940-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 M茅todo McCabe-Thiele colmuna destilaci贸n - Curso gratutito de simulaci贸n de procesos qu铆micos https://es.slideshare.net/slideshow/mtodo-mc-cabe-thiele-colmuna-destilacion-simulacion-procesos-quimicos/39911195 metodomccabe-thielecolmunadestilacion-simulacionprocesosquimicos-141006031440-conversion-gate02
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]>
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]> Mon, 06 Oct 2014 03:14:40 GMT https://es.slideshare.net/slideshow/mtodo-mc-cabe-thiele-colmuna-destilacion-simulacion-procesos-quimicos/39911195 CAChemE@slideshare.net(CAChemE) M茅todo McCabe-Thiele colmuna destilaci贸n - Curso gratutito de simulaci贸n de procesos qu铆micos CAChemE COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metodomccabe-thielecolmunadestilacion-simulacionprocesosquimicos-141006031440-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.
from CAChemE
]]> 7000 2 https://cdn.slidesharecdn.com/ss_thumbnails/metodomccabe-thielecolmunadestilacion-simulacionprocesosquimicos-141006031440-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Curso inciaci贸n a COCO Simulator y ChemSep - Simulaci贸n de procesos qu铆micos por orenador https://es.slideshare.net/slideshow/curso-inciacion-a-coco-simulator-y-chemsep-simulacion-de-procesos-quimicos-por-orenador/39641316 cursoinciacionacocosimulatorychemsep-simulaciondeprocesosquimicospororenador-140929035308-phpapp02
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]>
COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.]]> Mon, 29 Sep 2014 03:53:08 GMT https://es.slideshare.net/slideshow/curso-inciacion-a-coco-simulator-y-chemsep-simulacion-de-procesos-quimicos-por-orenador/39641316 CAChemE@slideshare.net(CAChemE) Curso inciaci贸n a COCO Simulator y ChemSep - Simulaci贸n de procesos qu铆micos por orenador CAChemE COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cursoinciacionacocosimulatorychemsep-simulaciondeprocesosquimicospororenador-140929035308-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> COCO Simulator en combinaci贸n con ChemSep permite la simulaci贸n de procesos qu铆micos de forma gratuita y se presenta como alternativa a Aspen y ChemCAD. Este curso presencial mostrar谩 su descarga e instalaci贸n as铆 como la resoluci贸n de ejemplos de menor a mayor grado de complejidad.
from CAChemE
]]> 12037 15 https://cdn.slidesharecdn.com/ss_thumbnails/cursoinciacionacocosimulatorychemsep-simulaciondeprocesosquimicospororenador-140929035308-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 C贸mo hacer una b煤squeda bibliogr谩fica en bases de datos cient铆ficas (Scopus y Web of Sciencie) https://es.slideshare.net/slideshow/como-hacer-una-busqueda-bibliografica-bases-datos-cientificos/32282781 comohacerunabusquedabibliograficabasesdatoscientificos-140313131400-phpapp01
Aprende a buscar art铆culos (papers) en bases de datos cient铆ficas. Se realizar谩 un ejemplo de b煤squeda gen茅rica y resultados en Scopus y Web of Science (WOK). Por 煤ltimo se dan algunos consejos a aquellos que se inician en invesitigaci贸n. Este material ha sido creado con motivo de la asignatura de "Pol铆meros Conductores" impartida en el M谩ster de Materiales de la Universidad de Alicante.]]>
Aprende a buscar art铆culos (papers) en bases de datos cient铆ficas. Se realizar谩 un ejemplo de b煤squeda gen茅rica y resultados en Scopus y Web of Science (WOK). Por 煤ltimo se dan algunos consejos a aquellos que se inician en invesitigaci贸n. Este material ha sido creado con motivo de la asignatura de "Pol铆meros Conductores" impartida en el M谩ster de Materiales de la Universidad de Alicante.]]> Thu, 13 Mar 2014 13:14:00 GMT https://es.slideshare.net/slideshow/como-hacer-una-busqueda-bibliografica-bases-datos-cientificos/32282781 CAChemE@slideshare.net(CAChemE) C贸mo hacer una b煤squeda bibliogr谩fica en bases de datos cient铆ficas (Scopus y Web of Sciencie) CAChemE Aprende a buscar art铆culos (papers) en bases de datos cient铆ficas. Se realizar谩 un ejemplo de b煤squeda gen茅rica y resultados en Scopus y Web of Science (WOK). Por 煤ltimo se dan algunos consejos a aquellos que se inician en invesitigaci贸n. Este material ha sido creado con motivo de la asignatura de "Pol铆meros Conductores" impartida en el M谩ster de Materiales de la Universidad de Alicante. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/comohacerunabusquedabibliograficabasesdatoscientificos-140313131400-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Aprende a buscar art铆culos (papers) en bases de datos cient铆ficas. Se realizar谩 un ejemplo de b煤squeda gen茅rica y resultados en Scopus y Web of Science (WOK). Por 煤ltimo se dan algunos consejos a aquellos que se inician en invesitigaci贸n. Este material ha sido creado con motivo de la asignatura de &quot;Pol铆meros Conductores&quot; impartida en el M谩ster de Materiales de la Universidad de Alicante.
from CAChemE
]]> 2478 13 https://cdn.slidesharecdn.com/ss_thumbnails/comohacerunabusquedabibliograficabasesdatoscientificos-140313131400-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Instalar Python 2.7 y 3 en Windows (Anaconda) https://es.slideshare.net/slideshow/instalar-python-27-y-3-en-windows-anaconda/31584987 python-windows-140224132049-phpapp01
驴C贸mo instalar Python en Windows? Diapositivas que explican c贸mo instalar paso a paso Python en Windows. Nota: Est谩n orientadas a cient铆ficos e ingenieros con poca experiencia en el entorno de windows.]]>
驴C贸mo instalar Python en Windows? Diapositivas que explican c贸mo instalar paso a paso Python en Windows. Nota: Est谩n orientadas a cient铆ficos e ingenieros con poca experiencia en el entorno de windows.]]> Mon, 24 Feb 2014 13:20:49 GMT https://es.slideshare.net/slideshow/instalar-python-27-y-3-en-windows-anaconda/31584987 CAChemE@slideshare.net(CAChemE) Instalar Python 2.7 y 3 en Windows (Anaconda) CAChemE 驴C贸mo instalar Python en Windows? Diapositivas que explican c贸mo instalar paso a paso Python en Windows. Nota: Est谩n orientadas a cient铆ficos e ingenieros con poca experiencia en el entorno de windows. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/python-windows-140224132049-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 驴C贸mo instalar Python en Windows? Diapositivas que explican c贸mo instalar paso a paso Python en Windows. Nota: Est谩n orientadas a cient铆ficos e ingenieros con poca experiencia en el entorno de windows.
from CAChemE
]]> 20947 12 https://cdn.slidesharecdn.com/ss_thumbnails/python-windows-140224132049-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 El uso de Python en la Ingenieria Qu铆mica - Charla Completa https://es.slideshare.net/slideshow/python-ingenieria-procesos-quimica/29797977 pythoningenieriaprocesosquimica-140108041427-phpapp02
Diapositivas para la charla completa: El uso de Python en Ingenier铆a Qu铆mica - PyConES 2013 Video en: http://www.youtube.com/watch?v=AGGaqjn9GuI En la conferencia de Python nacional (PyConES) que se celebr贸 en Madrid se propuso la introducci贸n te贸rica y resoluci贸n de ejemplos mediante Python de problemas cl谩sicos presentes en ingenier铆as. La resoluci贸n de ecuaciones diferenciales parciales (EDPs) mediante m茅todos num茅ricos permite obtener soluciones a problemas t铆picos presentes en diferentes fen贸menos f铆sicos como la propagaci贸n del sonido o del calor, la electrost谩tica, la electrodin谩mica, la din谩mica de fluidos, la elasticidad, etc. Programas de modelado algebraico permiten la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos en sector industrial qu铆mico, a la gesti贸n log铆stica de una empresa gen茅rica. Resoluci贸n de ecuaciones EDO (ecuaci贸n diferencial ordinaria) para el dise帽o de reactores qu铆micos. Se introducen as铆 Python y sus librer铆as con el objetivo de mostrar su potencial actual. ]]>
Diapositivas para la charla completa: El uso de Python en Ingenier铆a Qu铆mica - PyConES 2013 Video en: http://www.youtube.com/watch?v=AGGaqjn9GuI En la conferencia de Python nacional (PyConES) que se celebr贸 en Madrid se propuso la introducci贸n te贸rica y resoluci贸n de ejemplos mediante Python de problemas cl谩sicos presentes en ingenier铆as. La resoluci贸n de ecuaciones diferenciales parciales (EDPs) mediante m茅todos num茅ricos permite obtener soluciones a problemas t铆picos presentes en diferentes fen贸menos f铆sicos como la propagaci贸n del sonido o del calor, la electrost谩tica, la electrodin谩mica, la din谩mica de fluidos, la elasticidad, etc. Programas de modelado algebraico permiten la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos en sector industrial qu铆mico, a la gesti贸n log铆stica de una empresa gen茅rica. Resoluci贸n de ecuaciones EDO (ecuaci贸n diferencial ordinaria) para el dise帽o de reactores qu铆micos. Se introducen as铆 Python y sus librer铆as con el objetivo de mostrar su potencial actual. ]]> Wed, 08 Jan 2014 04:14:27 GMT https://es.slideshare.net/slideshow/python-ingenieria-procesos-quimica/29797977 CAChemE@slideshare.net(CAChemE) El uso de Python en la Ingenieria Qu铆mica - Charla Completa CAChemE Diapositivas para la charla completa: El uso de Python en Ingenier铆a Qu铆mica - PyConES 2013 Video en: http://www.youtube.com/watch?v=AGGaqjn9GuI En la conferencia de Python nacional (PyConES) que se celebr贸 en Madrid se propuso la introducci贸n te贸rica y resoluci贸n de ejemplos mediante Python de problemas cl谩sicos presentes en ingenier铆as. La resoluci贸n de ecuaciones diferenciales parciales (EDPs) mediante m茅todos num茅ricos permite obtener soluciones a problemas t铆picos presentes en diferentes fen贸menos f铆sicos como la propagaci贸n del sonido o del calor, la electrost谩tica, la electrodin谩mica, la din谩mica de fluidos, la elasticidad, etc. Programas de modelado algebraico permiten la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos en sector industrial qu铆mico, a la gesti贸n log铆stica de una empresa gen茅rica. Resoluci贸n de ecuaciones EDO (ecuaci贸n diferencial ordinaria) para el dise帽o de reactores qu铆micos. Se introducen as铆 Python y sus librer铆as con el objetivo de mostrar su potencial actual. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pythoningenieriaprocesosquimica-140108041427-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Diapositivas para la charla completa: El uso de Python en Ingenier铆a Qu铆mica - PyConES 2013 Video en: http://www.youtube.com/watch?v=AGGaqjn9GuI En la conferencia de Python nacional (PyConES) que se celebr贸 en Madrid se propuso la introducci贸n te贸rica y resoluci贸n de ejemplos mediante Python de problemas cl谩sicos presentes en ingenier铆as. La resoluci贸n de ecuaciones diferenciales parciales (EDPs) mediante m茅todos num茅ricos permite obtener soluciones a problemas t铆picos presentes en diferentes fen贸menos f铆sicos como la propagaci贸n del sonido o del calor, la electrost谩tica, la electrodin谩mica, la din谩mica de fluidos, la elasticidad, etc. Programas de modelado algebraico permiten la resoluci贸n de diferentes problemas que van desde la selecci贸n 贸ptima de equipos y recursos en sector industrial qu铆mico, a la gesti贸n log铆stica de una empresa gen茅rica. Resoluci贸n de ecuaciones EDO (ecuaci贸n diferencial ordinaria) para el dise帽o de reactores qu铆micos. Se introducen as铆 Python y sus librer铆as con el objetivo de mostrar su potencial actual.
from CAChemE
]]> 21988 8 https://cdn.slidesharecdn.com/ss_thumbnails/pythoningenieriaprocesosquimica-140108041427-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Reactor de flujo piston con MATLAB Octave https://es.slideshare.net/slideshow/dia6-octave-upm/29146156 dia6octaveupm-131212081738-phpapp02
Ejercicio propuesto para el 6潞 d铆a del taller]]>
Ejercicio propuesto para el 6潞 d铆a del taller]]> Thu, 12 Dec 2013 08:17:38 GMT https://es.slideshare.net/slideshow/dia6-octave-upm/29146156 CAChemE@slideshare.net(CAChemE) Reactor de flujo piston con MATLAB Octave CAChemE Ejercicio propuesto para el 6潞 d铆a del taller <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dia6octaveupm-131212081738-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Ejercicio propuesto para el 6潞 d铆a del taller
from CAChemE
]]> 12950 6 https://cdn.slidesharecdn.com/ss_thumbnails/dia6octaveupm-131212081738-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Reactor flujo piston en MATLAB - Octave - Craqueo termico https://es.slideshare.net/slideshow/reactor-flujo-piston-en-matlab-octave/29073147 rfp-131210080246-phpapp01
Ejercicio propuesto para el 4潞 d铆a del taller de modelado de reactores qu铆micos con MATLAB - Octave UPM]]>
Ejercicio propuesto para el 4潞 d铆a del taller de modelado de reactores qu铆micos con MATLAB - Octave UPM]]> Tue, 10 Dec 2013 08:02:46 GMT https://es.slideshare.net/slideshow/reactor-flujo-piston-en-matlab-octave/29073147 CAChemE@slideshare.net(CAChemE) Reactor flujo piston en MATLAB - Octave - Craqueo termico CAChemE Ejercicio propuesto para el 4潞 d铆a del taller de modelado de reactores qu铆micos con MATLAB - Octave UPM <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rfp-131210080246-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Ejercicio propuesto para el 4潞 d铆a del taller de modelado de reactores qu铆micos con MATLAB - Octave UPM
from CAChemE
]]> 11483 8 https://cdn.slidesharecdn.com/ss_thumbnails/rfp-131210080246-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Simulaci贸n de reactores qu铆micos con octave https://es.slideshare.net/slideshow/simulacin-de-reactores-qumicos-con-octave/28886877 simulacindereactoresqumicosconoctave-131204091821-phpapp01
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]]> Wed, 04 Dec 2013 09:18:21 GMT https://es.slideshare.net/slideshow/simulacin-de-reactores-qumicos-con-octave/28886877 CAChemE@slideshare.net(CAChemE) Simulaci贸n de reactores qu铆micos con octave CAChemE <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/simulacindereactoresqumicosconoctave-131204091821-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
from CAChemE
]]> 12785 11 https://cdn.slidesharecdn.com/ss_thumbnails/simulacindereactoresqumicosconoctave-131204091821-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-CAChemE-48x48.jpg?cb=1547619040 CAChemE es una comunidad formada por ingenieros qu铆micos (profesionales, docentes y estudiantes) que pretende estimular las posibilidades de software en la ingenier铆a de procesos y organizaci贸n industrial. Nuestro objetivo es promover las ventajas de las nuevas herramientas de software libre disponibles y fomentar su uso tanto en la industria como en la universidad. www.cacheme.org https://cdn.slidesharecdn.com/ss_thumbnails/shortcoursegbdminlp19alicante-190116061825-thumbnail.jpg?width=320&height=320&fit=bounds CAChemE/mixedinteger-and-disjunctive-programming-ignacio-e-grossmann Mixed-integer and Disj... https://cdn.slidesharecdn.com/ss_thumbnails/shortcourseps19alicante-190116061310-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/mixedinteger-models-for-planning-and-scheduling-ignacio-e-grossmann/128142640 Mixed-integer Models f... https://cdn.slidesharecdn.com/ss_thumbnails/introductiontochemicalprocesssimulators-tutorialonsimulationofchemicalreactors-coco-dwsim-aspen-hysy-161010081251-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/simulation-of-chemical-rectors-introduction-to-chemical-process-simulators-coco-dwsim-aspen-hysys-free-course/66948165 Simulation of Chemical...