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Recent advances in statistical procedures, coupled with the availability of high performance computational resources and the large mass of data generated from high throughput screening, have enabled a new paradigm for building mathematical models of the kinetic behavior of catalytic reactions. A Bayesian approach is used to formulate the model building problem, estimate model parameters by Monte Carlo based methods, discriminate rival models, and design new experiments to improve the discrimination and fidelity of the parameter estimates. The methodology is illustrated with a typical, model building problem involving three proposed Langmuir−Hinshelwood rate expressions. The Bayesian approach gives improved discrimination of the three models and higher quality model parameters for the best model selected as compared to the traditional methods that employ linearized statistical tools. This paper describes the methodology and its capabilities in sufficient detail to allow kinetic model builders to evaluate and implement its improved model discrimination and parameter estimation features.

We derive a dynamic model for roller compaction process based on Johanson's rolling theory, which is used to predict the stress and density profiles during the compaction and the material balance equation which describes the roll gap change. The proposed model considers the relationship between the input parameters (roll pressure, roll speed, and feed speed) and output parameters (ribbon density and thickness), so it becomes possible to design, optimize, and control the process using the model-based approach. Currently, the operating conditions are mostly found by trial and error. The simulation case studies show the model can predict the ribbon density and gap width while varying roll pressure, feed speed, and roll speed. The roll pressure influences the ribbon density much more than roll speed and feed speed, and the roll gap is affected by all three input parameters. Both output variables are very insensitive to the fluctuation of inlet bulk density. If the ratio of feed speed to roll speed is kept constant, neither ribbon density nor gap width change, but the production rate changes proportionally with feed speed. Based on observations from simulations, a control scheme is proposed. Furthermore, Quality by Design of the roller compactor can be achieved by combining this model and optimization procedure.

Pharmaceutical product development is a critical step in the path of a drug therapy from its discovery to its delivery to the patient. It is capital-intensive, time-consuming, and extremely information-and knowledgeintensive. This presents various challenges to manage the information and knowledge involved in a systematic, reusable, and user-friendly manner. Knowledge, in this context, means decision-making knowledge and mathematical knowledge that capture the families of mathematical models that exist in this domain. In this paper, which is the first of this two-part series of papers, we describe OntoMODEL, which is an ontological tool for mechanistic mathematical model management that facilitates systematic and standardizable methods for model storage, usage, and solution. [Suresh and co-workers have presented discussions on OntoMODEL at AIChE meetings in San Francisco, CA (2006) and Salt Lake City, UT (2007), as well as at the 18th European Symposium on Computer-Aided Process Engineering (ESCAPE-18) in Lyon, France (2008).] While the declarative knowledge in mathematical models is captured using ontologies, the procedural knowledge required for solving these models is handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well-established and the approach-intuitive; therefore, they do not require user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more-advanced applications such as model-based fault diagnosis, model predictive control (which is decribed in the second paper of this series), knowledge-based decisionmaking, and process flowsheet simulation, making it a useful tool in the intelligent automation of process operations. This paper describes the framework and use of OntoMODEL and discusses how it overcomes the shortcomings of existing approaches toward managing mathematical modeling knowledge.