Edward S. Blurock
A common thread throughout my career is the combination of chemistry, mathematics and computer science resulting in a multidisciplinary career focusing primarily (but not exclusively) on physical organic chemical modeling. However, a common thread of my research, independent of domain, is to attempt to mimic how a researcher approaches analysis. These multidisciplinary skills form the basis of CHEMCONNECT. In addition, have expertise across fields allows me to be an efficient liaison between fields.
Through my modeling activities I have developed a deep and intuitive knowledge of chemical processes. This is exemplified by my degrees from the University of California, Irvine, in biology (Bach. Sci), Chemistry (Bach. Art) and theoretical chemistry (Ph.D) and from Lund University the title of docent in physical organic and combustion chemistry. My modeling and mathematical abilities were honed at the University of Linz, Austria, where I focused in, but not exclusively, on applications of state-of-the-art techniques, for example artificial intelligence, machine learning, data representation and data management, to production quality control in the Steel industry (financed by EU and the Austrian Steel industry) and to chemical information applications (initially financed by the Austrian chemical industry). During a European Union project under which my fuzzy logic machine learning (ANALYSIS) was developed I was working in close connection the Fuzzy Logic Lab Linz under Prof. Peter Klement (my assistant was employed by this institute). My modeling, mathematical and software technical skills were used to develop detailed combustion kinetic models, mostly in cooperation with Combustion Physics at the University of Lund, simplified mathematical models of complex combustion processes, started at the Energy Sciences Department of the University of Lund and a web based tool for the determination of thermodynamic properties based on chemical bonding structure (basically a '2D graphical' lewis structure representation started through several 3 month research visits to the CNRS of Nancy, France.
A common denominator in my work is modeling and using state of the art software technical methods to accomplish the task at hand. One view of my research is to mimic human reasoning with software technical means. In combustion modeling, this manifests itself in a software expert and database system that mimics the way a combustion engineer would model large reaction mechanisms. In data analysis, I focus on fuzzy logic based methods to try to quantify, through classification and decision processes, human intuitive concepts
One primary advantage of my multidisciplinary background is that I can communicate in the language of chemists, mathematicians, computer scientists and engineers. The primary disadvantage is that, seen from the viewpoint of each of the disciplines, that he am an expert in the 'other' field. Chemists will tag me as a computer scientist. And, for example, at RISC in Austria, a computer science/mathematical institute, I was mostly tagged as a chemist.
ChemConnect2017 is an advanced state-of-the-art combustion database and data repository, derived from mechanistic, kinetic and thermodynamic chemical data (both experimental and modelling), organised into a network of interconnecting concepts. ChemConnect2017 goes beyond traditional repositories by providing a platform promoting data availability, searching and exchange. Through the parsing of the data sets, the pieces of information within data sets not only available to state of the art keyword searching algorithms, but through the use of semantic web techniques, also provides interconnections between independent data sets for efficient data exchange and comparison.
ChemConnect2019 is a smart cloud-based repository of experimental, theoretical and computational data. ChemConnect2019 goes beyond traditional data repositories in that the data is parsed and analyzed with respect to an extensive chemical and combustion knowledge base (an ontology). The goal is to have all data associated with experiments, from a device description, to the intermediate data (both computed and measured) and to their associated interpretations and the procedures and methodologies to the final published results and references to be available. Having published data linked to its dependent measurements and constants, devices, subsystems, sensors and even people and laboratories provides an effective accountability and more confidence in the data.
The purpose of this project is to calculate thermodynamic quantities, to be used in combustion mechanisms, from 2D-graphical forms of the molecular species. This project has its roots in the JTHERGAS system of Frederique Battin-LeClerc where in a cooperation JTHERGAS was developed. The current project is to re-implement JTHERGAS on a cloud platform providing all the tools needed for individually or collectively enhance the basic database of information needed to perform the calculation. The cloud version is found at http://www.2dthermodynamics.info (though due to cost reasons, the database may be offline, contact Edward S. Blurock, if you are interested). Further information (also under development) is available here.
REACTION, a system for automatically generating hydrocarbon combustion mechanisms has it roots in the original REACT program from 1995 written in C. In the early 2000's a C++ layer was added, including the ANALYSIS++ system, a system for machine learning (see for example, recognizing chemical regimes through clustering or for Phase optimized skeletal mechanisms for engine simulations) and JAVA applets and web services to produce decane and hexadecane mechanisms.
The current project is aimed at implementing the entire functionality of REACTION on a cloud-based system. Progress is currently focused on the implementations of the API accessing the REACT and REACTION C and C++ programs.
Previous Software Projects
My previous software development within the physical organic and kinetic chemistry fields form the backbone of my current work of database design for combustion kinetic researchers.
My (non-traditional) highly multi-disciplinary background spans at least four major divisions, chemistry, computer science, mathematics and engineering. This varied background promotes innovation with the application of new ideas coming from other fields which have the potential to be applied to combustion kinetics. In addition, it promotes interdisciplinary communication, where I can talk the language of all these multidisciplinary fields in their own technical language.
The four major focuses of my career have been:
1. Computational Chemistry This was my theses at the University of California, Irvine under Professor Warren Hehre (a major developer of the STO-3G basis set and now founder of Wavefunction), on the frozen core approximation and the (automatic generation and reduction of two electron integrals for d-orbitals).
2. Computer-Aided Organic Synthesis (CAOS) My initial work at RISC (Research Institute for Symbolic Computation, an institute famous for its fundamental work in computer algebra systems), was developing (in contract with Chemie Linz), a system for computer-aided organic synthesis, CAOS. This work produced software to analyze organic structure databases (for example REACCS, from Molecular Design) to automatically determine the reactive center given a specific reaction and to use this information in generating possible synthetic pathways.
3. Machine Learning and Quality Control While at RISC, but working (and being paid) through technology transfer company, machine learning techniques (and the complete software system ANALYSIS++) were developed to analyze industrial data for quality control. The first project being with Fischer Skis (relating ski physical properties with performance) and the second with Voest Alpine, Linz (quality control in rolling mills). The later project culminated in the EU project REFORM. The primary output, was the principle investigator for a particular deliverable, with one student working under him, was the ANALYSIS++ software system. This is a highly object oriented software system for managing a wide variety of machine learning and statistical algorithms.
4. Combustion Kinetics In parallel with other work (unfunded, other than a very short, 3 man-month, project with ÖMV, the Austrian oil company) at RISC (late 90's), the work in CAOS was transformed to be used in the automatic generation of oxidative combustion mechanisms for large hydrocarbons. This preliminary work led to a cooperation, and a eventual move to Lund University. I have since been working in Lund, financed by several European and VR projects, in developing new kinetic mechanisms and developing methods to efficiently implement these mechanisms into complex computational fluid dynamic codes.
5. Chemical Kinetic Database: My experience in developing the data structures to represent, generate and manage detailed combustion kinetic has lead me to recognize the need for a kinetic database beyond that being offered to the community at this time (for example the National Institute of Standards kinetic database). The goal of the project is to make the entire range of combustion research data available. This means not just accepted standard values, but values that have changed, values under different contexts (for example, reaction rates and reactions as they are used in different mechanisms) and historical values. In addition, more complex models such as detailed combustion mechanisms are not treated as a coarse grained single entity, but decomposed into fine grained data, such as individual species with their corresponding thermodynamic data, reactions with their corresponding reaction constants. The goal of the database should also not just be storage of data constants, but these constants are also stored in a form that can help in the analysis of, for example detailed mechanisms.
6. Tabulation of kinetic information: The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single 'generic' ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occuring in the ignition process differ only in their 'timing'. By 'morphing' the time scale, the profile shapes can be made to align. From the aligned profile shapes a generic or 'average' profile can be derived. Ignition progress times are modified by synchronizing events. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific 'normalized' times. The exact species behavior under differing conditions is treated as a perturbation from this 'generic' curve. Parameterizing such perturbations result in simpler and more accurate relationships and, more importantly, fewer governing parameters. The ignition progress, however, is still just one parameter. In this normalized time representation, a particular ignition progress value always represents the exact same event, regardless of conditions. This has consequences when 'mixing' two different conditions. Under the normalized time, for example, the expected average mixed state profiles can be accurately reproduced. When not normalized, perturbed profiles are produced.
7. Machine Learning and e-commerce recommendation systems: Through an industrial project with APPTUS, machine learning techniques, primarily clustering, have been used to add insight into forming general recommendation of a similar class of products. The results have been implemented and tested on the APPTUS test platform.