Venkat Venkatasubramanian

7006834244

Publications - 5

Innovation through intelligent computer-aided formulation design

Naz P. Taskiran Kris Prasopsanti Thunyaras Phanusupawimol Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Current Opinion in Chemical Engineering

Publication Date: 2025-03-01

Volume: 47

Issue: Unknown

Page Range: Unknown

Description:

This perspective paper presents a focused review of a selected topic of chemical-based products, namely, formulations. As formulations cover a wide range of chemical-based products, we highlight opportunities for innovation in three types of formulations — liquid blends, which are mixtures of chemicals that are in the liquid state at standard conditions; liquid formulations, which are mixtures of chemicals that may exist in different states but the final product is a single-phase liquid; and emulsions, which are also mixtures of chemicals that may exist in different states, but the final product is in the form of an emulsion. In each case, we discuss aspects of design, analysis, and innovation together with issues and challenges that could be tackled to find better and more sustainable products. In particular, the potential of hybrid artificial intelligence augmented computer-aided techniques that can aid in the design, analysis, and innovation of formulations is highlighted.

Open Access: Yes

DOI: 10.1016/j.coche.2025.101099

A Perspective on Artificial Intelligence for Process Manufacturing

Vipul Mann Jingyi Lu Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Engineering

Publication Date: 2025-09-01

Volume: 52

Issue: Unknown

Page Range: 60-67

Description:

To achieve sustainable development goals and the requirements of a circular economy, a new class of intelligent computer-aided methods and tools is needed. Artificial intelligence (AI) techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing. However, the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed. In this perspective paper, we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing, with a focus on chemical product design, process synthesis and design, process control, and process safety and hazards.

Open Access: Yes

DOI: 10.1016/j.eng.2025.01.014

An Improved Machine Learning Model for Pure Component Property Estimation

Xinyu Cao Anjan Tula Xi Chen Ming Gong Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Engineering

Publication Date: 2024-08-01

Volume: 39

Issue: Unknown

Page Range: 61-73

Description:

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

Open Access: Yes

DOI: 10.1016/j.eng.2023.08.024

eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning

Mauricio Sales-Cruz Vipul Mann Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Computers and Chemical Engineering

Publication Date: 2024-02-01

Volume: 181

Issue: Unknown

Page Range: Unknown

Description:

Process flowsheet synthesis, design, and simulation require integrated approaches that combine domain knowledge and data-driven methods for fast, efficient, and reliable solutions. However, due to the recent surge in data and machine learning capabilities, there has been a shift towards building purely data-driven systems for process flowsheet synthesis and related problems. Such approaches have certain drawbacks. Here, we present a hybrid method that combines data-driven approaches with domain knowledge to represent process flowsheets and solve problems related to process synthesis, design, and simulation. We present an extended SFILES (or eSFILES) representation, a multi-level hierarchical flowsheet representation with varying degrees of process knowledge. At level 0, flow diagrams are represented as purely text-based SFILES strings. At level 1, the SFILES grammar, along with inferencing algorithms, is used to construct a flowsheet hypergraph explicitly representing flow diagram connectivity. At level 2, specifications needed for material and energy balance calculations are introduced, and, after simulation, the results are also added using annotated flowsheet hypergraphs. Finally, at level 3, a process ontology is connected with the annotated flowsheet hypergraph to include design and operation parameters as well as the detailed simulation results. We discuss this hierarchical framework using several case studies.

Open Access: Yes

DOI: 10.1016/j.compchemeng.2023.108505

Hybrid Artificial Intelligence-based Process Flowsheet Synthesis and Design using Extended SFILES Representation

Mauricio Sales-Cruz Vipul Mann Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Computer Aided Chemical Engineering

Publication Date: 2024-01-01

Volume: 53

Issue: Unknown

Page Range: 1279-1284

Description:

Process flowsheet synthesis and design involves simultaneously solving several problems, including determining the unit operations and their sequence, underlying reactions and reaction stoichiometry, downstream separation design and operation parameters, sustainability factors, and many more. Naturally, this results in a large amount of data being associated with a given process flowsheet that captures the relevant process context and should be readily accessible. This data is useful for solving related problems both using data-driven and process knowledge-based methods. A hierarchical framework, called the extended SFILES (or eSFILES), proposed recently stores this information using a combination of text-based, graph-based, and ontology-based representations. Here, we provide details on a prototype software for automated flowsheet representation and generation across various levels in the eSFILES framework. The underlying methods include a novel flowsheet grammar, a set of inferencing algorithms, and interfacing with a commercial process simulator facilitating rigorous flowsheet simulation.

Open Access: Yes

DOI: 10.1016/B978-0-443-28824-1.50214-3