About

Rohit Ramachandran is a Professor in the Department of Chemical and Biochemical Engineering at Rutgers University. His research interests encompass process systems engineering, process control, process simulation, process optimization, mathematical modelling, population balance modelling, pharmaceutical engineering, particulate and chemical processes, biological systems, and high-performance computing. He has received numerous honors including the AIChE PD2M Drug Product QbD Award in 2018, the Rutgers Board of Trustees Award for Research Excellence in 2017, and the NSF CAREER Award in 2014. Dr. Ramachandran completed his Ph.D. in Chemical Engineering with a Diploma of Imperial College from Imperial College London, and holds degrees from the National University of Singapore. His professional experience includes positions as Assistant Professor, Associate Professor with tenure, and Professor at Rutgers, along with a postdoctoral associate role at MIT. His work focuses on advancing process engineering through experimental studies, validation, and the development of innovative modelling and control strategies.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Machine Learning
• Statistics
• Manufacturing engineering
• Chemistry
• Optics
• Biological system
• Chromatography
• Business
• Operations management
• Biochemical engineering
• Data Mining
• Physics
• Process management
• Medicine
• Biotechnology
• Telecommunications
• Risk analysis (engineering)
• Systems engineering
• Reliability engineering
• Mathematical optimization

Selected publications

• Optimization of key energy and performance metrics for drug product manufacturing

  International Journal of Pharmaceutics · 2022 · 28 citations

  • Computer Science
  • Process engineering
  • Computer Science
  PublisherOA PDFDOI

• Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies

  Journal of Chemical Technology & Biotechnology · 2021 · 71 citations

  • Computer Science
  • Manufacturing engineering
  • Computer Science

  Abstract Continuous bioprocessing is significantly changing the biological drugs (or biologics) manufacturing landscape by potentially improving product quality, process stability, and overall profitability, as was similarly seen during the adoption of advanced manufacturing processes for small molecule drugs in the past decade. However, the implementation of continuous manufacturing for biological processes producing protein‐based drug molecules, such as monoclonal antibodies (mAbs), is facing several new hurdles. The barriers to continuous bioprocessing can be overcome through improved process understanding via better predictive capabilities enabled by hybrid modeling that can also lead to robust process control. This review article summarizes the recent advances and ongoing obstacles faced during the use of advanced process analytical technologies (PAT), process modeling, and control strategies to enable continuous manufacturing of mAbs. In addition, this review also discusses the process strategies and future directions of advanced continuous manufacturing approaches that have been adapted by other industries and that could be implemented for mAbs production soon. © 2021 Society of Chemical Industry (SCI).

  DOI

• Sampling optimization for blend monitoring of a low dose formulation in a tablet press feed frame using spatially resolved near-infrared spectroscopy

  International Journal of Pharmaceutics · 2021 · 18 citations

  • Computer Science
  • Materials science
  • Biological system
  DOI

• Performance assessment of linear iterative optimization technology (IOT) for Raman chemical mapping of pharmaceutical tablets

  Journal of Pharmaceutical and Biomedical Analysis · 2021 · 14 citations

  • Computer Science
  • Artificial Intelligence
  • Chemistry
  DOI

• Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

  Processes · 2020 · 244 citations

  • Computer Science
  • Manufacturing engineering
  • Artificial Intelligence

  The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.

  DOI

• A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

  AIChE Journal · 2020 · 43 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Abstract Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black‐box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical‐based approach (Jack‐knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.

  DOI

Recent grants

• EAGER:REAL-D: Smart Decision Making using Data and Advanced Modeling Approaches

  NSF · $255k · 2018–2023

• CAREER: Multi-scale modeling and analysis of reactive granulation processes

  NSF · $462k · 2014–2021

Frequent coauthors

• Marianthi Ierapetritou

  38 shared

• Ravendra Singh

  Rutgers, The State University of New Jersey

  36 shared

• Fernando J. Muzzio

  Rutgers, The State University of New Jersey

  27 shared

• Anwesha Chaudhury

  Novartis (United States)

  22 shared

• Lalith Kotamarthy

  Rutgers, The State University of New Jersey

  21 shared

• Dana Barrasso

  Siemens (United Kingdom)

  18 shared

• František Štěpánek

  University of Chemistry and Technology, Prague

  18 shared

• Maitraye Sen

  17 shared

Labs

• Research Centers and Labs at Rutgers Department of Chemical and Biochemical EngineeringPI

Awards & honors

• AIChE PD2M Drug Product QbD Award, 2018
• Rutgers Board of Trustees Award for Research Excellence, 201…
• Chancellor’s Scholar Award for Outstanding Scholarship, 2017
• Outstanding CBE Faculty Award, 2015
• CBE Best Teacher/Mentor/Advisor Award, 2015

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