Santhoji Katare

Sulfur vulcanization of natural rubber for benzothiazole accelerated formulations: From reaction mechanisms to a rational kinetic model

Abstract The chemistry of accelerated sulfur vulcanization is reviewed and a fundamental kinetic model for the vulcanization process is developed. The vulcanization of natural rubber by the benzothiazolesulfenamide class of accelerators is studied, where 2-(morpholinothio) benzothiazole (MBS) has been chosen as the representative accelerator. The reaction mechanisms that have been proposed for the different steps in vulcanization chemistry are critically evaluated with the objective of developing a holistic description of the governing chemistry, where the mechanisms are consistent for all reaction steps in the vulcanization process. A fundamental kinetic model has been developed for accelerated sulfur vulcanization, using population balance methods that explicitly acknowledge the polysulfidic nature of the crosslinks and various reactive intermediates. The kinetic model can accurately describe the complete cure response including the scorch delay, curing and the reversion for a wide range of compositions...

A hybrid genetic algorithm for efficient parameter estimation of large kinetic models

Abstract The development of predictive models is a time consuming, knowledge intensive, iterative process where an approximate model is proposed to explain experimental data, the model parameters that best fit the data are determined and the model is subsequently refined to improve its predictive capabilities. Ascertaining the validity of the proposed model is based upon how thoroughly the parameter search has been conducted in the allowable range. The determination of the optimal model parameters is complicated by the complexity/non-linearity of the model, potentially large number of equations and parameters, poor quality of the data, and lack of tight bounds for the parameter ranges. In this paper, we will critically evaluate a hybrid search procedure that employs a genetic algorithm for identifying promising regions of the solution space followed by the use of an optimizer to search locally in the identified regions. It has been found that this procedure is capable of identifying solutions that are essentially equivalent to the global optimum reported by a state-of-the-art global optimizer but much faster. A 13 parameter model that results in 60 differential-algebraic equations for propane aromatization on a zeolite catalyst is proposed as a more challenging test case to validate this algorithm. This hybrid technique has been able to locate multiple solutions that are nearly as good with respect to the “sum of squares” error criterion, but imply significantly different physical situations.

Catalyst design: knowledge extraction from high-throughput experimentation

We present a new framework for catalyst design that integrates computer-aided extraction of knowledge with high-throughput experimentation (HTE) and expert knowledge to realize the full benefit of HTE. We describe the current state of HTE and illustrate its speed and accuracy using an FTIR imaging system for oxidation of CO over metals. However, data is just information and not knowledge. In order to more effectively extract knowledge from HTE data, we propose a framework that, through advanced models and novel software architectures, strives to approximate the thought processes of the human expert. In the forward model the underlying chemistry is described as rules and the data or predictions as features. We discuss how our modeling framework—via a knowledge extraction (KE) engine— transparently maps rules-to-equations-to-parameters-to-features as part of the forward model. We show that our KE engine is capable of robust, automated model refinement, when modeled features do not match the experimental features. Further, when multiple models exist that can describe experimental data, new sets of HTE can be suggested. Thus, the KE engine improves (i) selection of chemistry rules and (ii) the completeness of the HTE data set as the model and data converge. We demonstrate the validity of the KE engine and model refinement capabilities using the production of aromatics from propane on H-ZSM-5. We also discuss how the framework applies to the inverse model, in order to meet the design challenge of predicting catalyst compositions for desired performance.  2003 Elsevier Science (USA). All rights reserved.

Spontaneous emergence of complex optimal networks through evolutionary adaptation

Abstract An important feature of many complex systems, both natural and artificial, is the structure and organization of their interaction networks with interesting properties. Such networks are found in a variety of applications such as in supply chain networks, computer and communication networks, metabolic networks, food webs, etc. Here, we present a theory of self-organization by evolutionary adaptation in which we show how the structure and organization of a network is related to the survival, or in general the performance, objectives of the system. We propose that a complex system optimizes its network structure in order to maximize its overall survival fitness which is composed of short- and long-term survival components. These in turn depend on three critical measures of the network, namely, efficiency, robustness and cost, and the environmental selection pressure. Fitness maximization by adaptation leads to the spontaneous emergence of optimal network structures, both power law and non-power law, of various topologies depending on the selection pressure. Using a graph theoretical case study, we show that when efficiency is paramount the “star” topology emerges and when robustness is important the “circle” topology is found. When efficiency and robustness requirements are both important to varying degrees, other classes of networks such as the “hub” emerge. This theory provides a general conceptual framework for integrating survival or performance objectives, environmental or selection pressure, evolutionary adaptation, optimization of performance measures and topological features in a single coherent formalism. Our assumptions and results are consistent with observations across a wide variety of applications. This framework lays the ground work for a novel approach to model, design and analyze complex networks, both natural and artificial, such as metabolic pathways, supply chains and communication networks.

An agent-based learning framework for modeling microbial growth

Abstract The overall idea of this paper is to study the intelligent behavior of microbes in a binary substrate environment with agent-based learning models. Study of microbial growth enables understanding of industrially relevant processes such as fermentation, biodegradation of pollutants, antibody production using hybridoma cells, etc. Artificial intelligence techniques such as genetic algorithms and agent-based learning methodologies have been used to study microbial growth. Specifically, the objective is to (1) qualitatively model the intelligent growth characteristics of the microbes using a minimal set of generic rules as against algebraic/differential mathematical relationships and (2) propose a suitable hypothesis that explains the origin of intelligence through learning in the microbes. A microbial cell has been modeled as a collection of agents characterized by a set of resources and an objective to survive and grow. The actions of the agents are governed by generic rules such as survival, growth and division as is common for any individual in a resource-limited competitive environment. The interaction of the agents with the environment and other fellow agents enables them to “learn” and “adapt” to the changes in the environment and thus defines the dynamics of the system. The origin of intelligence in the microbes has been studied by both a simple learning rule of imitation and rule discovery studies.

A systematic framework for the design of reduced-order models for signal transduction pathways from a control theoretic perspective

Systematic study of cellular signaling pathways facilitates improved understanding of processes including cell proliferation, metabolism and embryonic development. Key cell signaling pathway characteristics, such as transduction, amplification, feedback, and filtering display striking similarities to that of a control system. This leads us to believe that a control theoretic analysis of these pathways could enable a systems level understanding and help identify the role of individual modules in controlling the overall cellular behavior. Towards this end, this paper presents a framework with a step-by-step bottom-up methodology to guide the development of modular reduced-order signaling pathway components that collectively predict key observations and yet are simple. Critical steps of this iterative method include (1) modification of the pathway structure by addition and/or deletion of key nodes and/or arcs, (2) critical evaluation of multiple functional forms for fluxes and (3) estimation of the pathway model parameters. The parameter estimation minimizes the mismatch between the desired behavior and the predicted behavior using a hybrid procedure that involves a genetic algorithm to identify interesting regions in the parameter-space that are further explored using a local optimizer. The utility of this framework has been demonstrated by developing a reduced-order model for the mitogen-activated protein kinase (MAPK) pathway in mouse NIH-3T3 fibroblasts. The reduced-order model, consisting of five ordinary differential equations and 16 parameters, quantitatively predicts the bistable and proportional MAPK responses to the PDGF stimulus at different levels of MAP kinase phosphatase (MKP).

Reaction modeling suite: A rational, intelligent and automated framework for modeling surface reactions and catalyst design

Abstract The continuing development of high throughput experiments (HTE) in the field of catalysis has dramatically increased the amount of data that can be collected in relatively short periods of time. The key questions in the current scenario are (1) Even when HTE can afford “Edisonian” discovery, how can the increasing amounts of data be converted to knowledge that will guide the next search in the vast design space that encompasses catalytic materials? and (2) How can HTE data lead to fundamental understanding? In order to address these questions, we propose a catalyst design architecture that involves (1) a forward model to predict the performance of a given material structure and (2) a genetic algorithm based inverse problem that uses the forward model to search the descriptor space for a material that meets specific design objectives. We have developed a rational, automated knowledge extraction (KE) engine to aid the forward model building process. The Reaction Modeling Suite (RMS; U.S. patent pending) is a set of tools based on artificial intelligence and optimization techniques that enables the expert to initiate the kinetic modeling sequence in a simple reaction chemistry language. The software then interprets this information into a reaction sequence, writes the appropriate equations, optimizes the model parameters while keeping them in physically and chemically allowed bounds, and does statistical analysis of the results. These steps have been demonstrated for propane aromatization on HZSM-5. A successful forward model has considerable value in its own right, but its power is dramatically leveraged by the inverse model, which forecasts successful specific catalyst formulations. This potential to truly design catalysts is the return on the investment in model building.