Process Informatics Research Articles

Process informatics tools for predictive modeling: Hydrogen combustion

AbstractPredictive modeling of chemical reaction systems is facilitated through development of an automated data‐centric infrastructure, Process Informatics Model (PrIMe). Modeling and analysis tools were built to bridge the PrIMe Data infrastructure with DataCollaboration, a framework designed to make inferences from experimental observations in the context of an underlying model. The developed tools were integrated with the PrIMe Workflow Application, allowing users to create and run drag‐and‐drop applications on the fly. Organizing the data, linking the data to scientific methods, and automating the analysis offer new venues of scientific inquiry, such as evaluating the consistency of heterogeneous data records, making uncertainty‐quantified predictions, quantifying the contribution of newly obtained or even hypothetical data to the question of interest, or testing similar “what–if” scenarios. The developed system is demonstrated for the chemical kinetics of hydrogen combustion. © 2011 Wiley Periodicals, Inc. Int J Chem Kinet 44: 101–116, 2012

Developing the PAH-PP soot particle model using process informatics and uncertainty propagation

In this work we present the new PAH-PP soot model and use a data collaboration approach to determine some of its parameters. The model describes the formation, growth and oxidation of soot in laminar premixed flames. Soot particles are modelled as aggregates containing primary particles, which are built from polycyclic aromatic hydrocarbons (PAHs), the main building blocks of a primary particle (PP). The connectivity of the primary particles is stored and used to determine the rounding of the soot particles due to surface growth and condensation processes. Two neighbouring primary particles are replaced by one if the coalescence level between the two primary particles reaches a threshold. The model contains, like most of the other models, free parameters that are unknown a priori. The experimental premixed flame data from Zhao et al. [B. Zhao, Z. Yang, Z. Li, M.V. Johnston, H. Wang, Proc. Combust. Inst. 30 (2) (2005) 1441–1448] have been used to estimate the smoothing factor of soot particles, the growth factor of PAHs within particles and the soot density using a low discrepancy series method with a subsequent response surface optimisation. The optimised particle size distributions show good agreement with the experimental ones. The importance of a standardised data mining system in order to optimise models is underlined.

Transforming data into knowledge—Process Informatics for combustion chemistry

The present frontier of combustion chemistry is the development of predictive reaction models, namely, chemical kinetics models capable of accurate numerical predictions with quantifiable uncertainties. While the usual factors like deficient knowledge of reaction pathways and insufficient accuracy of individual measurements and/or theoretical calculations impede progress, the key obstacle is the inconsistency of accumulating data and proliferating reaction mechanisms. Process Informatics introduces a new paradigm. It relies on three major components: proper organization of scientific data, availability of scientific tools for analysis and processing of these data, and engagement of the entire scientific community in the data collection and analysis. The proper infrastructure will enable a new form of scientific method by considering the entire content of information available, assessing and assuring mutual scientific consistency of the data, rigorously assessing data uncertainty, identifying problems with the available data, evaluating model predictability, suggesting new experimental and theoretical work with the highest possible impact, reaching community consensus, and merging the assembled data into new knowledge.

Prediction of Secondary Metabolism Pathways from Genomic Information and Compounds Data

Genome sequencing projects have determined complete genomic sequences of more than 20 organisms. Now we obtain catalogs of complete gene sets of these species. But we really want to know is not only catalogs of each gene or protein. What we want to know is how they express biological functions. Biological function can be understood only by the overall behavior of an organic system. Thus we must understand how biological system is constructed from elemental genes and molecules and the interaction of these molecules. Pathway informatics or process informatics is a methodology to understand the construction and the behavior of the living system based on informatics. The aim of pathway informatics includes the construction of pathway databases like KEGG [5], the development of pathway reconstruction method in silico, and the simulation of biological system using above databases and methods. To achieve the third goal, our current knowledge is insufficient. Limiting to the metabolic system, we relatively well know about primary metabolism, but we have little knowledge about secondary metabolism. Drug metabolism is one of the example we want to reveal, for we must develop drugs with good efficacy and least toxicity. Here we propose a method to predict uncharacterized metabolic pathways from genetic information and compounds data.