Climate models use mathematical equations to represent interactions between and within the ocean, land, ice, and atmosphere — providing a means to understand the climate system and predict future climate changes. They are becoming increasingly...
Today's CFD Programs are more powerful and easier to use than ever, thanks to improved mesh-generation strategies, computational methodologies, user interfaces and enhanced graphics capabilities. Cover photo courtesy of Acusim and Intelligent Light.
This article focuses on the regulatory drivers for alarm management, summarizes key practices, and challenges automation vendors to devise new ways to help customers implement more-effective alarm systems.
In silicocatalyst optimization is the ultimate application of computers in catalysis. This article provides an overview of the basic concepts of predictive modeling and describes how this technique can be used in catalyst and reaction design.
Computer-based environmental compliance training offers a number of advantages. This article discusses the benefits of computer-based training, outlines its most important components, and provides tips for structuring and writing a course.
By employing the latest virtualization solutions for process control systems, chemical plants can reduce PC hardware requirements, simplify system management, and lower the total cost of ownership, as well as improve site availability, reliability,...
Transforming the performance maps to a reduced coordinate system that is independent of suction conditions and rotational speeds allows these curves to be accurately incorporated into a process simulator.
Computational fluid dynamics has moved from mainframes to PCs and laptops. Newer and better software lets you conduct analyses not possible before, and regular engineers, not just experts, can now carry out CFD.
The flow fields in multiphase separators are often extremely complicated, making it difficult to model such systems. Computational fluid dynamics (CFD) and visual dynamic modeling (VDM) can be used together to overcome some of the challenges.
Dynamic process modeling based on non-uniformly sampled data and multiple sampling runs, even for nonlinear systems, can be performed using spreadsheet implementations of the weighted-least-squares (WLSQ) and Runge-Kutta WLSQ methods.