Schmidt alix (33 resultados)

Condición: New.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New.

Condición: New.

Hardcover. First Edition. Quarto, 151 pages. In Very Good condition. Bound in publisher's tan cloth boards with black lettering. Mild general shelf wear. Textblock clean. Shelved on the Front Table. 1411496. Shelved Dupont Bookstore.

Hardcover without dustjacket as issued, 99 illustrations, as new condition; clean and crisp; no internal marks. Foreign shipping may be extra.

Condición: New.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Taschenbuch. Condición: Neu. Neuware - In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science. - Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of success - Discusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beverages - Addresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterization - Covers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methods - Discusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definition - Features case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforce This book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle.…

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

Condición: New. In English.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Hardback. Condición: New. In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle.…

Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condición: Brand New. 368 pages. 9.19x6.13x9.21 inches. In Stock.

Hardback. Condición: New. In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle.…

Hardcover. Condición: New.

first edition, small folio, tan cloth boards with black lettering to spine & front cover; 161pp, illus, VG+ (protected by plastic adhesive cover, slight marks on board edges where plastic has come off, slight soiling to page edges, v. good condition internally, photos are v. clean & bright) 161.

Paperback. Condición: new. Paperback. In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle. This book offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. It describes activities related to creating new or improved functional material products by discovering new ingredients or new ingredient combinations resulting in targeted quality properties. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle. This book offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. It describes activities related to creating new or improved functional material products by discovering new ingredients or new ingredient combinations resulting in targeted quality properties. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle. This book offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. It describes activities related to creating new or improved functional material products by discovering new ingredients or new ingredient combinations resulting in targeted quality properties. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle. 364 pp. Englisch.…

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.