Computer-Aided Modeling of Chemical and Biological Systems:  Methods, Tools, and Applications (original) (raw)

Modeller—An Interactive Model Editor for Physical-Chemical-Biological Process Models

Modeller is a tool that implements a systematic approach to model design. It builds on network modelling of physical-chemical-biological processes and currently supports lumped system dynamics, orderof-magnitude assumption handling, DAE-index reduction and instantiation of simulation models generating MatLab files for immediate solving with its DAE-1 solvers.

A Modeling Framework for Chemical Process Design Through a Computer-Aided System

2004

This paper deals with a modeling environment for systematic chemical process design through a Computer-Aided Modeling System (CAMS), whose potential is highlighted through a case study. In particular, the capability of ICAS-MoT (an integrated modeling environment) to build, analyze, manipulate, solve and visualize mathematical models is shown. Our main interest is to use models for process design and analysis by testing and implementing them as fast as possible (in a reliable and efficient manner), writing the model equations without any programming effort, generating modules that can be exported through a model transfer feature to other simulation engines and/or external software, and implementing several process/model configurations in the same environment. From the application perspective, the modeling, operational analysis and process configuration of an emulsion polymerization reactor is considered. The case study highlights the advantages of using a computer-aided modeling sys...

Specification , Implementation and Evaluation of a Tool for Multiple Modeling

2009

Model-based problem solvers have received increasing attention in various application domains and for different tasks. Since model building is a laborious and time-consuming endeavor, creating appropriate models is the main obstacle faced by the attempt to build a model-based problem solver for industrial application. Although some tools have been already developed and applied in industrial projects, an integrated environment or toolbox for model development and integration, and algorithms required for generating and transforming models are still lacking. The Model Manipulation Environment (MOM) project was initiated, whose goal is to close this gap. It aims at offering a software tool that provides generic interfaces for manipulation and transformation of models. These include generic modules that enable automated generation of qualitative models from a number of numerical simulators such as MatlabTM/SimulinkTM. The abstract interfaces can also be used to integrate other existing t...

Mathematical Modeling of Chemical Processes Table 2.1. A Systematic Approach for Developing Dynamic Models

Mathematical Model (Eykhoff, 1974) "a representation of the essential aspects of an existing system (or a system to be constructed) which represents knowledge of that system in a usable form" Everything should be made as simple as possible, but no simpler. General Modeling Principles • The model equations are at best an approximation to the real process. • Adage: "All models are wrong, but some are useful." • Modeling inherently involves a compromise between model accuracy and complexity on one hand, and the cost and effort required to develop the model, on the other hand. • Process modeling is both an art and a science. Creativity is required to make simplifying assumptions that result in an appropriate model. • Dynamic models of chemical processes consist of ordinary differential equations (ODE) and/or partial differential equations (PDE), plus related algebraic equations. 1. State the modeling objectives and the end use of the model. They determine the required levels of model detail and model accuracy. 2. Draw a schematic diagram of the process and label all process variables. 3. List all of the assumptions that are involved in developing the model. Try for parsimony; the model should be no more complicated than necessary to meet the modeling objectives. 4. Determine whether spatial variations of process variables are important. If so, a partial differential equation model will be required. 5. Write appropriate conservation equations (mass, component, energy, and so forth). 6. Introduce equilibrium relations and other algebraic equations (from thermodynamics, transport phenomena, chemical kinetics, equipment geometry, etc.). 7. Perform a degrees of freedom analysis (Section 2.3) to ensure that the model equations can be solved. 8. Simplify the model. It is often possible to arrange the equations so that the dependent variables (outputs) appear on the left side and the independent variables (inputs) appear on the right side. This model form is convenient for computer simulation and subsequent analysis. 9. Classify inputs as disturbance variables or as manipulated variables. Modeling Approaches • Physical/chemical (fundamental, global)

A Workbench to Experiment on New Model Engineering Applications

Lecture Notes in Computer Science, 2003

There are many different tools that support, in one way or another, the Unified Modeling Language (UML), but most of these tools are targeted to software developers. The System Modeling Workbench (SMW) [8] is a collection of tools targeted to those interested in doing research on new modeling languages and constructing tools to transform and derive new artifacts from models in those languages.

An integrated modelica environment for modeling, documentation and simulation

1998

Modelica is a new object-oriented multi-domain modeling language based on algebraic and differential equations. In this paper we present an environment that integrates different phases of the Modelica development lifecycle. This is achieved by using the Mathematica environment and its structured documents, "notebooks". Simulation models are represented in the form of structured documents, which integrate source code, documentation and code transformation specifications, as well as providing control over simulation and result visualization. Import and export of Modelica code between internal structured and external textual representation is supported. Mathematica is an interpreted language, which is suitable as a scripting language for controlling simulation and visualization. Mathematica also supports symbolic transformations on equations and algebraic expressions which is useful in building mathematical models.

A Matlab software framework for dynamic model emulation

Environmental Modelling & Software, 2012

The paper describes a software framework for implementing the main stages of the Data Based Mechanistic (DBM) modelling approach to the reduced order emulation (meta-modelling) of large dynamic system computer models, within the Matlab software environment. The framework exploits routines in the CAPTAIN Toolbox to identify and estimate transfer function models that reflect the dominant modes of the dynamic behaviour in the large model. This allows for the 'nominal emulation' and validation of the large model for a single, specified set of parameters; as well as 'stand-alone, full emulation' based on the construction and validation of hyper-dimensional maps between a user-specified range of large model parameters and the parameters of the associated, low order transfer function models. The software framework uses the multivariable structure constructs available within Matlab Ô to form a small library of routines that will become part of the Captain Toolbox. The library is formed around special data structures that facilitate multivariable operations and visualisations which both enhance the efficiency of the emulation modelling analysis and the modeller's interaction with the process of emulation. The nature of the analysis is illustrated by a topical example concerned with the emulation of the OTIS computer simulation model for the transport and dispersion of solutes in a river system.