Dr. Sriram Devanathan is the Head of Center for Excellence in Advanced Materials and Green Technologies and Professor & Vice-chair in the Department of Chemical Engineering and Materials Science. He comes to Amrita with ten years of industrial experience at the 3M Company in Minnesota, Texas and California (USA) and seven years experience in academic research as a graduate student at Iowa State University, USA, where he also received his Ph. D. He has a Certificate of Six Sigma Black Belt Training from 3M, and has successfully led quality improvement/cost reduction projects in various 3M Company manufacturing plants in the US, Canada, Singapore and Japan. His areas of interest include Low-cost Materials, Waste-to-Energy Technologies, Process Intensification,Six Sigma, Fault Diagnosis, Empirical Modeling, Data Mining, Statistical Process Control, Design of Experiments, and Process Modeling & Simulation.

He has trained over 600 engineers, managers and operators in statistical methodology and software and Six Sigma. Dr. Sriram has significant expertise in CGMP (Current Good Manufacturing Practices) and Internal Auditing. He is the author of several publications in US technical journals.



1997 Ph. D., Chemical Engineering & Statistics Iowa State University, Ames, Iowa, USA
1993 M. S., Chemical Engineering Iowa State University, Ames, Iowa, USA
1990 B. Tech. (Chemical Engineering) Osmania University, Hyderabad, India


  • Six Sigma Black Belt Training, 3M, USA
  • ISO 9001 - Internal Auditor Training, USA
  • Advanced Statistical Process Control, SPC Inc., Tennessee, USA
  • Industrial Design of Experiments, SPC Inc., Tennessee, USA
  • 3M Pollution Prevention Pays Award, 3M, USA



  • Chemical Engineering Process Optimization  
  • Chemical Engineering Transport
  • Low-cost green materials & technologies


Data Reconciliation & Gross Error Detection

A chemical engineering plant contains a complex network of flow channels and storage units. For reasons of cost control, optimal operation, regulations, and safety, it is important and necessary to ensure material and energy accountability. The occurrence of measurement errors (fixed and random), as well as physical leaks, can hinder the accountability. Thus, our team is focused on finding ways to ensure accountability, with reasonable accuracy, in the presence of the obscuring factors. Techniques involving a combination of fundamental concepts of chemical engineering combined with statistical methods, are used to achieve the objectives, for various common scenarios: steady state processes, transient (dynamic) processes, and processes in which measurements of process parameters manifest serial correlation (due to the establishment of an engineering process control system, or due to other reasons). Other efforts include application of multivariate statistical methods to model predictive relationships useful in a chemical engineering context (one specific application is to detect the occurrence of adulteration of diesel).

Plastics Reclamation and Conversion

The rapidly growing consumption rate, combined with lack of adequate techniques & practices for recycling, indicates a vast opportunity for technology intervention aimed at plastics reclamation and conversion to virgin monomers or virgin polymers. Our group works on low cost technology alternatives for this purpose - specifically selective dissolution & reprecipitation and pyrolytic gasification. We also study intensified methods for separation and dissolution, such as with ultrasonics.

Groundwater Transport

Studies aimed at mathematical representation of transport phenomena assume importance in numerous applications. We are currently focused on hydrogeological transport, especially in the context of specific types of groundwater formations - seen in conjunction with soil properties, vegetation, and climatic conditions.


  • Data reconciliation
  • Measurement biases
  • Process intensification
  • Pyrolytic gasification
  • Plastics recycling
  • Transport phenomena
  • Statistical process modeling


Sl.No Name of the Student Topic
1 Niranchana V., Niveditha Manoj
Second stage: E. Harshita, Mythili A.,Swetha N.
Low-cost paper from water hyacinth (with applications for sanitary pads)
2 Preethi P., Muralidarran P. V., Jayanthan T. P., Pravin R. Development of home-scale pyrolytic gasification unit for conversion of plastic waste to fuel
3 Gangatharan M., Lakshmi Narayanan E. R., Prakash M. Sythesis of low-cost biodegradable superabsorbent polymers


Publication Type: Journal Article
Year of Publication Publication Type Title
2014 Journal Article M. Raj Subramaniam, Devanathan, S., and Kumaresan, D., “Synthesis of micrometer-sized hierarchical rutile TiO 2 flowers and their application in dye sensitized solar cells”, Royal Society of Chemistry (RSC) Advances, vol. 4, pp. 36791–36799, 2014.[Abstract]

Cactus-like hierarchical rutile TiO2 flowers and three dimensional (3D) highly branched rutile TiO2 nanorods with sizes measuring up to 5 microns were synthesized on conductive substrates by a facile hydrothermal route without the presence of a surfactant or template. These samples with different morphologies and microstructures were studied by X-ray powder diffraction (XRD), field emission-scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM). We also studied the photovoltaic performances of these samples by using them as photoanodes in dye-sensitized solar cells (DSSCs). The highly branched TiO2 nanorod based photoanode in DSSCs showed a power conversion efficiency of 3.07% which was significantly higher than that of the cactus TiO2 flower based (2.66%) photoanode. The electrochemical impedance spectroscopy (EIS) analysis of the interfacial charge transfer kinetics in these photoanodes in DSSCs showed higher recombination resistance (R2) and longer electron lifetime in highly branched nanorods. The enhancement of the efficiency of the highly branched TiO2 nanorod photoanode based DSSC compared to that of cactus TiO2 flower DSSC is mainly attributed to the superior light scattering capability, fast electron transfer and longer electron lifetime with suppressed recombination.

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2014 Journal Article S. Devanathan, Elangovan, M., and Aditya, T. N., “A Study on the Effect of Processing Parameters on the Quality of Parts made by Low Cost 3D Printer (submitted for publication)”, International Journal of Research in Engineering and Technology, 2014.
2014 Journal Article S. Devanathan, J., S., K., P. Marimuthu, and I., R. K., “Raw Material Preheating by a Novel Energy Recycling Method in Metal Casting, with the Application of Design of Experiments and Regression Analysis (Accepted)”, Journal of Cleaner Production, 2014.
2013 Journal Article E. J. Jelmy, Ramakrishnan, S., Devanathan, S., Rangarajan, M., and Kothurkar, N. K., “Optimization of the conductivity and yield of chemically synthesized polyaniline using a design of experiments”, Journal of Applied Polymer Science, vol. 130, pp. 1047-1057, 2013.[Abstract]

<p>The electrical conductivity and yield of polyaniline (PANi) were optimized using a design of experiments (DOE). PANi samples were synthesized by the chemical oxidative polymerization of aniline using methane sulfonic acid as the dopant acid and ammonium persulfate as the oxidant. The main factors in the synthesis of PANi that can affect the conductivity were identified as (i) the concentration of dopant acid, (ii) oxidant-to-monomer ratio, and (iii) the addition rate of oxidant to monomer. Using a Box-Behnken DOE method the regression equation, main effects plots, contour plots, and optimization plots for conductivity and yield were generated and analyzed. Under the optimized conditions of dopant acid concentration of 0.9M, an oxidant addition rate of 30 mL/h and an OM ratio of 0.9, PANi with a conductivity of 1.95 S/cm and yield of 95% was obtained. The observed trends in the four-point probe conductivity measurements were correlated with the polymer structure using fourier transform infrared spectroscopy, X-ray diffraction studies, and scanning electron microscopy. © 2013 Wiley Periodicals, Inc.</p>

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2012 Journal Article M. Balachandran, Devanathan, S., Muraleekrishnan, Rb, and Bhagawan, S. Sa, “Optimizing properties of nanoclay-nitrile rubber (NBR) composites using Face Centred Central Composite Design”, Materials and Design, vol. 35, pp. 854-862, 2012.[Abstract]

<p>The properties of acrylonitrile butadiene copolymer (NBR)-nanoclay composites were modelled using response surface methodology (RSM). A Face Centred Central Composite Design (FCCD) with four factors and three levels was used to obtain the relationship between nanocomposite properties and levels of ingredients. The factors considered in the design were silica content, nanoclay content, vulcanization system and dicumyl peroxide content. The nanocomposites were evaluated for tensile strength, modulus, elongation at break, oxygen permeation rate and effect of oil ageing on mechanical properties. Regression equations were generated to model the properties of interest and generate response surfaces and contour plots. The predicted properties of the nanocomposites were in good agreement with the experimental results. The contour plots were overlaid within the applied constraints to identify the combination of factor ranges that gives the optimal performance of the nanocomposites for application in control system bladders for launch vehicle applications. © 2011 Elsevier Ltd.</p>

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2005 Journal Article S. Devanathan, Vardeman, S. B., and Rollins, D. K., “Likelihood and Bayesian Methods for Accurate Identification of Measurement Biases in Pseudo Steady-State Processes”, Chemical Engineering Research and Design, vol. 83, pp. 1391–1398, 2005.[Abstract]

Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes. Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement. The first method is based on a likelihood ratio test for the presence of a mean shift. The second is based on a Bayesian decision rule (relying on prior distributions for unknown parameters) for the detection of a mean shift. The performance of these two methods is compared with that of a method given by Devanathan et al. (2000). For the process studied, both techniques were found to have higher identification power than the method of Devanathan et al. and appears to have excellent but sightly lower type I error performance than the Devanathan et al. method.

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2002 Journal Article D. K. Rollins, Devanathan, S., and Bascuñana, M. Victoria B., “Measurement bias detection in linear dynamic systems”, Computers & chemical engineering, vol. 26, pp. 1201–1211, 2002.[Abstract]

A new method to detect the existence of biased measured variables in dynamic processes is presented. Hence, this work presents a new Dynamic Global Test (DGT) and test procedure for dynamic gross error detection (GED) that brings to light certain of its attributes which have not hitherto (to our knowledge) been presented in GED literature. Recognition of these attributes leads to a scheme that enables identification of the type of biased measurement (e.g. flow or level). This approach is not computationally intensive and is applicable in the case of process leaks and multiple biased variables. Simulation results for the identification of the type of biased measurement (e.g. flow or level) and the estimation of the time of occurrence (ETOC) are given. The performance study in this work specifically varied the size of measurement bias (δi), the bias location (i), the bias true time of occurrence (TTOC), the significance level (α), and the sample size (N). This study shows the proposed approach to be accurate in identifying the type of biased variable and its TTOC. The performance of the proposed scheme improves as N and δi increase.

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2000 Journal Article S. Devanathan, Rollins, D. K., and Vardeman, S. B., “A new approach for improved identification of measurement bias”, Computers & Chemical Engineering, vol. 24, pp. 2755–2764, 2000.[Abstract]

This work presents a technique that can completely and accurately identify measurement bias in cases where it is not possible
to use the method of Rollins and Davis (1992, 1993) and where the method of Narasimhan and Mah (1987) fail to perform
accurately. This technique makes use of information contained in the relationship between individual measurements and the
corresponding nodal imbalance. The performance of this method is demonstrated on a problem from the literature that has been
difficult for other methods to handle. In addition, this article discusses how the new technique can be used as a visual monitoring
tool for identifying biased measured variables.

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1996 Journal Article D. K. Rollins, Cheng, Y., and Devanathan, S., “Intelligent selection of hypothesis tests to enhance gross error identification”, Computers & chemical engineering, vol. 20, pp. 517–530, 1996.[Abstract]

The objective of this study was to evaluate the ability of a new technique to identify systematic measurement errors (i.e. biases) in process variables. This technique obtains high identification accuracy and computational speed by efficiently selecting a small subset of statistical hypothesis tests from a very large set using new selection criteria developed in this work. In this article the proposed technique is also evaluated and compared to a well known method in a fairly extenisve Monte Carlo simulation study. The proposed technique was found to be computationally faster and, as the variances of measurement errors decreased, significantly more accurate in identifying systematic errors.

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1993 Journal Article D. K. Rollins and Devanathan, S., “Unbiased estimation in dynamic data reconciliation”, AIChE journal, vol. 39, pp. 1330–1334, 1993.[Abstract]

A computationally fast technique accurately estimates process variables when conditions are dynamic due to changes in steady states. The process variable estimators are unbiased and have known distributions. Thus, confidence intervals for true values of process variables are provided. The formulation of this technique was motivated by a recursive, dynamic data reconciliation technique that obtains very accurate estimators. These two techniques are compared in terms of computational speed and accuracy of estimators. The proposed technique is computationally faster, but not as accurate when variances of process measurements are large. However, the accuracy of the proposed estimators is shown to approach that of the recursive technique by iteratively recalculating estimates and when measurement variances decrease.

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Publication Type: Conference Proceedings
Year of Publication Publication Type Title
2014 Conference Proceedings S. Devanathan, Gupta, P. T. R., and V., S., “An Approach for the Assessment of India as a Global Oil & Gas R&D Destination”, International Conference on Advances in Chemical Engineering and Technology . Elsevier Publication, TKM College of Engineering, p. 392, 2014.[Abstract]

This paper demonstrates how existing knowledge on technical and business developments can be organized, categorized, and subsequently used to determine key assessment indicators to better understand the state of R&D. Thus, it offers an approach to then employ the indicators for benchmarking of India versus other leaders in oil & gas R&D, with the intention of identifying gaps and opportunities for innovative as well as incremental advances in important R&D areas. A few examples of such historical innovations are given, and the approach when employed for India then results in a SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) - a summary that can be greatly beneficial as a starting point for planning and implementation efforts aimed at improvement of R&D contributions in this sector.

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Publication Type: Conference Paper
Year of Publication Publication Type Title
1993 Conference Paper D. K. Rollins and Devanathan, S., “Computational issues in gross error detection and data reconciliation”, in International Conference on Foundations of Computer-Aided Process Operations FOCAPO, Crested Butte, Colorado, 1993.



  • Member of Panel Discussion on "Innovation Pedagogy: Adaptive Inter-Disciplinary Curriculum" at T4E (International Conference on Technology for Education) by IEEE, at Amritapuri campus, Amrita Vishwa Vidyapeetham, Kollam, on 21st December, 2014.
  • Keynote address at INCCOM 2014 at VSSC, Thiruvananthapuram, 14-15 November, 2014
  • Member of Panel Discussion on "Chemical Engineering - Traditional and Beyond" at ICACE TKM'14, at TKM College of Engineering, 16-18 October, 2014.
  •  “Is Sustainable Development an Oxymoron?”, International Conference on Renewable Energy Resources for 21st Century, March 9-10, 2012, Amrita Vishwa Vidyapeetham, Coimbatore.
  • “Challenges and Solutions in the Implementation of Learning Management Systems in India”, International Conference on Technology Enhanced Education, January 4, 2012, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam.
  • Two-day workshop conducted on “Quality Control for Composites” – Vikram Sarabhai Space Center, Trivandrum, October 25 – 26, 2010.
  • Plenary talk, “Six Sigma – concepts and applications for chemical engineering” at the International Conference on “Recent Advances in Chemical Engineering and Technology (RACET-2011)”, on March 10, 2011, Kochi, India.
  • Research Methodology – Keynote Address (Chief Guest) at “FRACTALS 10”, Department of Electronics and Communication Engineering, Sona College of Technology, 7th October 2010.
  • Quality Control for Composites – Invited Talk delivered at ISAMPE National Conference on Composites (INCCOM8), 5th December 2008, Trivandrum.
  • Advances in Mechanical Sciences – Kumaraguru College of Technology, Keynote Address, 23rd March 2007.
  • National Seminar on “Biopolymers: Source, Structure & Applications in Biotechnology,” Bannari Amman  Institute of Technology, Sathyamangalam, Tamil Nadu, Keynote Address, 19th July 2007.
  • Co-authored a Paper at MACRO 2006 – 9th Conference of Polymers for Advanced Technologies – NCL Pune, 18th December 2006.
  • Featured Speaker at AIChE Fall Conference 1997 (Chicago, Illinois, USA)
  • Presented a Poster AIChE Fall Conference 1993 (St. Louis, Missouri, USA)


  • Heat Transfer in Chemical Engineering
  • Chemical Technology
  • Organic Chemical Technology
  • Transport Phenomena
  • Mechanical Operations
  • Process Intensification
  • Numerical Methods in Chemical Engineering
  • Statistical Design of Experiments
  • Statistical Methods for Researchers
  • Probability and Statistical Inference


Dr. Sriram Devanathan's technical interests revolve around applying statistical way of thinking to solving problems in chemical engineering and other areas, with sustainable technologies and approaches. The focus is three-fold: process modeling & optimization, materials development for low-cost socially impactful applications, and reclamation of waste materials.

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