Two afternoon poster sessions will be held: Tuesday, August 1, and Wednesday, August 2. All posters will be put up for BOTH poster sessions, and the posters will remain in place for viewing and discussion from 8am Tuesday until 1:30pm Thursday. Authors with an odd poster ID number will be at their posters on Tuesday and those with an even poster ID will be at on Wednesday.
For poster presenters, information about poster format and display are at the bottom of this page.
The detailed poster lists are provided here. There are links to abstracts for each poster, or you can open a PDF booklet of all the abstracts. Note that the poster presenter is listed in bold:
Poster ID |
Title |
Authors |
Affiliation |
1 (Tue) |
The Unified Active Learning Framework for Molecular Material Design (abstract) |
Jiali Li,1 Shomik Verma,2 Kevin Greenman,2 Yizhe Chen,3 Haoyu Yin,3 Zhihao Wang,3 Rafael Gomez- Bombarelli,2 Aron Walsh,4Xiaonan Wang3 |
1. National University of Singapore, Singapore, Singapore; 2. Massachusetts Institute of Technology, Cambridge, MA, USA; 3. Tsinghua University, Beijing, China; 4. Imperial College London, London, United Kingdom |
2 (Wed) |
Descriptive Process Attribute Sequence to Sequence Rolling for Zero-Shot Fault Diagnosis (abstract) |
Wen-An Lee,1 Yuan Yao,1 Jia-Lin Kang2 |
1. National Tsing Hua University, Hsinchu, Taiwan; 2. National Yunlin University of Science and Technology, Yunlin, Taiwan |
3 (Tue) |
Discriminative Edge-Group Sparse Principal Component Analysis for Process Fault Diagnosis (abstract) |
Po-Chun Mao, Yuan Yao |
National Tsing Hua University, Hsinchu, Taiwan |
4 (Wed) |
Overcoming Modeling Challenges in Thermoset Shape-Memory Polymers: A Machine-Learning Approach (abstract) |
Ama Darkwah, Cheng Yan, Patrick Mensah |
Southern University and A&M College, Baton Rouge, LA, USA |
5 (Tue) |
Application of Experimental Design via Bayesian Optimization to Pharmaceutical Process Characterization (abstract) |
Jose Tabora-Sierra, Jun Li, Jason Stevens, Michael Williams, Jiayi Fu, Damian Reyes, Dimitri Skliar |
Bristol Myers Squibb, New Brunswick, NJ, USA |
6 (Wed) |
Predicting Thermal Conductivity of Additively Manufactured Alloys using Machine Learning-Based Models (abstract) |
Evelyn Quansah, Patrick Mensah, Congyuan Zeng |
Southern University and A&M College, Baton Rouge, LA, USA |
7 (Tue) |
Automated Outlier Detection and Estimation of Missing Data (abstract) |
Jinwook Rhyu, Dragana Bozinovski, Alexis Dubs, Naresh Mohan, Elizabeth Cummings Bende, Andrew Maloney, Miriam Nieves, Jose Sangerman, Amos Lu, Moo Sun Hong, Anastasia Artamonova, Rui Ou, Paul Barone, James Leung, Jacqueline Wolfrum, Anthony Sinskey, Stacy Springs, Richard Braatz |
Massachusetts Institute of Technology, Cambridge, MA, USA |
8 (Wed) |
A Mixed-Integer Reformulation for Global Training and Regularization of Deep Learning under Data Scarcity (abstract) |
Hasan Sildir,1 Ozgun Deliismail2 |
1. Gebze Technical University, Kocaeli, Turkey; 2. SOCAR Turkey R&D and Innovation Co., Izmir, Turkey |
9 (Tue) |
Physics-Guided Machine-Learning Model Predictive Control of a High-Density Poly-Ethylene Slurry Reactor (abstract) |
Zhen-Feng Jiang,1David Shan-Hill Wong,1 Yuan Yao,1 Jia-Lin Kang,2 Yao-Cheng Chuang,3 Shi-Shang Jang,1 John Di-Yi Ou3 |
1. Department of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan; 2. Department of Chemical and Materials Engineering, National Yunlin University of Science and Technology, Yun-Ling, Taiwan; 3. Center for Energy and Environmental Research, National Tsing Hua University, Hsinchu, Taiwan |
10 (Wed) |
FARM: A Fast, Accurate, Robust Fault Detection and Diagnosis Framework for Industrial Process Monitoring (abstract) |
Alireza Miraliakbar, Zheyu Jiang |
Oklahoma State University, Stillwater, OK, USA |
11 (Tue) |
Fault Detection and Identification for Chemical Process Based on 3D-CNN with Continuous Wavelet Transform (abstract) |
Chinatsu Ukawa, Yoshiyuki Yamashita |
Tokyo University of Agriculture and Technology, Tokyo, Japan |
12 (Wed) |
Machine Learning to Determine Solid Properties of Slurries through Raman Attenuation of Solution Phase (abstract) |
Rupanjali Prasad, Steven Crouse, Ronald Rousseau, Martha Grover |
Georgia Institute of Technology, Atlanta, GA, USA |
13 (Tue) |
Development of Algorithms for Mass-Constrained Dynamic Neural Networks (abstract) |
Angan Mukherjee, Debangsu Bhattacharyya |
West Virginia University, Morgantown, WV, USA |
14 (Wed) |
Optimal Control Strategies for Industrial Nonlinear Processes: Reinforcement Learning -Fuzzy Optimized Artificial Neural Networks (abstract) |
Jiaxin Zhang, Rangaiah Gade Pandu, Lakshminarayanan Samavedham |
National University of Singapore, Singapore, Singapore |
15 (Tue) |
A Novel Framework to Determine Complex Process Feasibility (abstract) |
Margherita Geremia,1 Fabrizio Bezzo,1 Marianthi G. Ierapetritou2 |
1. CAPE-Lab – Computer-Aided Process Engineering Laboratory. Department of Industrial Engineering, University of Padova, Padova, Italy; 2. Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA |
16 (Wed) |
A Novel Machine-Learning-Based Method for Tracking Renewable Carbon during Biomass Co-Processing (abstract) |
Liang Cao,1 Bhushan Gopaluni,1 Yankai Cao,1 Siang Lim2 |
1. The University of British Columbia, Vancouver, BC, Canada; 2. Parkland Refinery, Vancouver, Canada |
17 (Tue) |
Optimization Of Modern Manufacturing for Thermoelectric Material Using Machine Learning And Data Science (abstract) |
Ke Wang, Alexander Dowling |
University of Notre Dame, South Bend, IN, USA |
18 (Wed) |
Efficacy of Computer-Aided Molecular Design (CAMD) by Large Language Model GPT-3.5 (abstract) |
Yuan-Cyun Liao,1Davidshan-Hill Wong,1 Jia-Lin Kang2 |
1. National Tsing Hua University, Hsinchu, Taiwan; 2. National Yun-lin University of Science and Technology, DOULIU, Taiwan |
19 (Tue) |
Machine-Learning-Powered Molecular Design: Optimal Solvents for Hybrid Extraction-Distillation (abstract) |
Johanna Lindfeld, Luca Bosetti, Benedikt Alexander Winter, Johannes Schilling, André Bardow |
ETH Zurich, Zurich, Switzerland |
20 (Wed) |
Learning the Best Configuration for Controlling Modular Dynamic Systems (abstract) |
Yi Dai, Andrew Allman |
University of Michigan, Ann Arbor, MI, USA |
21 (Tue) |
Industrial Process Monitoring using Deep-Learning-Based Process Analytics and Feature Extraction (abstract) |
Cheng Ji,1 Fangyuan Ma,1,2 Jingde Wang,1 Wei Sun1 |
1. College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, China; 2. Center of process monitoring and data analysis, Wuxi Research Institute of Applied Technologies, Tsinghua University, Wuxi, China |
22 (Wed) |
Bayesian Optimization for Nonlinear Model Calibration (abstract) |
Montana Carlozo, Bridgette Befort, Edward Maginn, Alexander Dowling |
University of Notre Dame, South Bend, IN, USA |
23 (Tue) |
A Multi-Language Process Monitoring Framework for Chemical Industry (abstract) |
Chengyu Han, Jingzhi Rao, Jingde Wang, Wei Sun |
College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, China |
24 (Wed) |
On the Identifiability of Hybrid Models (abstract) |
Kyla Jones, Alexander Dowling |
University of Notre Dame, Notre Dame, IN, USA |
25 (Tue) |
Application of Bibliometric Data-Analysis Software in Coronary Heart Disease Research (abstract) |
Sam Li,1 Si Ying Lim1 |
NUS, Singapore, Singapore |
26 (Wed) |
Exploiting High-Throughput Experiments in Bayesian Optimization (abstract) |
Leonardo Gonzalez, Victor Zavala |
University of Wisconsin-Madison, Madison, WI USA |
27 (Tue) |
Blind Source Separation for Applications in Real-Time IR Spectroscopy Monitoring (abstract) |
Steven Crouse,1 Stefani Kocevska,1 Sean Noble,2 Rupanjali Prasad,1 Anthony Howe,2 Dan Lambert,2 Ronald Rousseau,1 Martha Grover1 |
1. Georgia Institute of Technology, Atlanta, GA, USA; 2. Savannah River National Laboratory, Aiken, SC, USA |
28 (Wed) |
Integrated Planning and Scheduling for Crude Oil Operations using Data-Driven Bilevel Multi-Follower Optimization (abstract) |
Hasan Nikkhah,1 Vasileios M. Charitopoulos,2 Styliani Avraamidou,3Burcu Beykal1 |
1. Department of Chemical & Biomolecular Engineering, Center for Clean Energy Engineering, University of Connecticut, Storrs, CT, USA; 2. Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, University College London, London, United Kingdom; 3. Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA |
29 (Tue) |
Model Identification from Historical Data using Data-Mining Techniques: Industrial Application (abstract) |
Ammar Bakhurji, Rohit Patwardhan, Manaf Al Ahmadi |
Saudi Aramco, Dhahran, Saudi Arabia |
30 (Wed) |
A Novel Training Methodology for Hybrid Models with Time-Varying Disturbances: An Application to Buildings (abstract) |
Pranav Krishna, Loren de la Rosa, Matthew Ellis |
University of California, Davis, Davis, CA, USA |
31 (Tue) |
Multiperiod Optimization of Integrated Energy Systems with Machine-Learning Surrogates to Predict Market Impacts (abstract) |
Xinhe Chen,1 Radhakrishna Gooty,2 Darice Guittet,3 Alexander Dowling1 |
1. University of Notre Dame, South Bend, IN, USA; 2. National Energy Technology Laboratory, Pittsburgh, PA, USA; 3. National Renewable Energy Laboratory, Golden, CO, USA |
32 (Wed) |
Improving System Identification of Kinetic Networks using Neural Stochastic Differential Equations (abstract) |
Krystian Ganko,1 Nathan Stover,1 Utkarsh,2 Richard Braatz,1 Christopher Rackauckas2,3 |
1. MIT Department of Chemical Engineering, Cambridge, MA, USA; 2. MIT CSAIL, Cambridge, MA, USA; 3. JuliaHub, Inc., Cambridge, MA, USA |
33 (Tue) |
Machine Learning for Sensor Data Integration and Quantitative Early Detection of Plant Diseases (abstract) |
Sina Jamalzadegan,1 Giwon Lee,1,2 Qingshan Wei1 |
1. North Carolina State University, Raleigh, NC, USA; 2. Kwangwoon University, Seoul, Republic of Korea |
34 (Wed) |
Applications of Machine Learning In Petroleum Systems: Optimization under Uncertainty (abstract) |
David Robbins |
Chevron, San Ramon, CA, USA |
35 (Tue) |
Bayesian Neural Network for Prediction of Protein–Ligand Dissociation Kinetics (abstract) |
Yujing Zhao, Qilei Liu, Lei Zhang, Jian Du, Qingwei Meng |
Dalian University of Technology, Dalian, China |
36 (Wed) |
Integrating Smart Manufacturing Techniques into Undergraduate Education (abstract) |
Mrunal Sontakke,1 Lucky E. Yerimah,1 Andreas Rebmann,1 Sambit Ghosh,1 Craig Dory,2 Ronald Hedden,1 B. Wayne Bequette1 |
1. Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; 2. CESMII Smart Manufacturing Innovation Center, Rensselaer Polytechnic Institute, Troy, NY, USA |
37 (Tue) |
Optimization in Engineering with Embedded Linear Model Decision Trees (abstract) |
Bashar Ammari,1 Emma Johnson,2 Georgia Stinchfield,1 Taehun Kim,3 Michael Bynum,2 William Hart,2 Joshua Pulsipher,1 Carl Laird1 |
1. Carnegie Mellon University, Pittsburgh, PA, USA; 2. Sandia National Laboratories, Albuquerque, NM, USA; 3. Georgia Institute of Technology, Atlanta, GA, USA |
38 (Wed) |
Enabling Enquiry-Based Learning through Online Visualization-Based Data Science Modules (abstract) |
Chin-Fei Chang,1 Joseph Menicucci,1 Raghuram Thiagarajan,2 Srinivas Rangarajan1 |
1. Lehigh University, Bethlehem, PA, USA; 2. Pratt & Miller, Detroit, MI, USA |
39 (Tue) |
Three-Body Problem: Small Data, Interpretation and Extrapolation In ML (abstract) |
Luis Briceno-Mena,1 Christopher Arges,2 Jose Romagnoli1 |
1. Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA, USA; 2. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA |
40 (Wed) |
Learning-Based Control in Atomic-Scale Processing of Semiconductor Materials (abstract) |
Praneeth Srivanth Ramesh,1 Shoubhanik Nath,1 Kwanghyun Cho,2 Ali Mesbah1 |
1. University of California, Berkeley, Berkeley, CA, USA; 2. Mechatronics Research, Samsung Electronics, Hwaseong-si, Korea, Republic of |
41 (Tue) |
A Machine-Learning Approach for Optimizing Flushing Operations in Lube-Oil Manufacturing and Packaging Plants (abstract) |
Swapana Jerpoth, Barnabas Gao, Emmanuel Aboagye, Robert Hesketh, Stewart Slater, Kirti Yenkie |
Rowan University, Glassboro, NJ, USA |
42 (Wed) |
A Surrogate-Based Framework for Feasibility-Driven Optimization (abstract) |
Huayu Tian, Marianthi Ierapetritou |
University of Delaware, Newark, DE, USA |
43 (Tue) |
Hybrid Modeling for Solid Transport Critical Velocity (abstract) |
Su Meyra Tatar,1 Yushi Deng,1 Haijing Gao,2 Selen Cremaschi1 |
1. Department of Chemical Engineering, Auburn University, Auburn, AL, USA; 2. Chevron Technical Center, Houston, TX, USA |
44 (Wed) |
Deep-Reinforcement-Learning-Based Adaptive Tuning of Control for Complex Dynamical Systems (abstract) |
Myisha Chowdhury, Qiugang (Jay) Lu |
Texas Tech University, Lubbock, TX, USA |
45 (Tue) |
Real-Time Guidance for Model Predictive Control System through Automated Analysis (abstract) |
K.C. Ridley |
Aspen Technology, Inc., Houston, TX, USA |
46 (Wed) |
Integrating Process Analytics and Machine Learning into The Chemical Engineering Curriculum (abstract) |
Aycan Hacioglu |
MathWorks, Natick, MA, USA |
47 (Tue) |
IoT Sensor Networks and Machine Learning for Analyzing Plant Response to Salt And Acetic-Acid Treatments (abstract) |
Chinwe Aghadinuno1 A’Ishah Trahan,2 Eman ElDakkak,1 Yadong Qi,1 Wesley Gray,1 Jiecai Luo,1 Yasser Ismail,1 Fred Lacy,1 Patrick Mensah1 |
1. Southern University and A&M College, Baton Rouge, LA, USA; 2. Louisiana State University/Shreveport, Baton Rouge, LA, USA |
48 (Wed) |
Dynamic Optimization of Complex Combustion Systems with Transformer Neural Networks (abstract) |
Ethan Gallup, Jacob Tuttle, Blake Billings, Jake Immonen, Kody Powell |
University of Utah, Salt Lake City, UT, USA |
49 (Tue) |
Utilizing Physics-Informed Machine Learning to Bridge Hydrodynamics and Hopping Transport Models In Ionic Liquids (abstract) |
Elvis Umaña, Ryan Cashen, Matthew Gebbie, Victor Zavala |
University of Wisconsin-Madison, Madison, WI, USA |
50 (Wed) |
Model-Based Design of Experiments and Pyomo.DoE (abstract) |
Hailey Lynch, Jialu Wang, Alexander Dowling |
University of Notre Dame, Notre Dame, IN, USA |
Poster Format
- The poster boards are sized 7.5 ft wide and 5.5 ft high, double-sided with TWO posters per poster-board side. This translates into FOUR posters per board. The poster boards will be on stands (see photo below).
- Thus the maximum poster display size is 42 in wide (106 cm) and 56 in tall (142 cm), but we recommend that you use somewhat smaller dimensions such as ≤40 in wide by ≤48 in tall. Note that the largest standard poster size at FedEx is 36in x 48in.
- Please consider using and adapting one of the two poster templates provided.
- There is a useful video about Morrison's alternative approach at this link: Morrison Approach
- All posters will be listed in the program and on the poster boards by their Poster ID Number (see poster listing above), which should be printed in the upper left corner of the poster.
- The poster boards will be double-sided with TWO posters per poster-board side. This translates into FOUR posters per board. The poster boards will be on stands.
- Use lightweight materials. Pushpins must be able to hold your poster when mounted to the board. Pushpins will be provided.
Display Instructions
- Mount your poster at its numbered location early on Tuesday morning, and leave it mounted through Thursday's sessions. At that time, it must be removed. Any poster not removed by 5:00 p.m. Thursday by the contact author will be removed by the staff and not returned.
- Be present to answer questions about your poster during the Tuesday poster session if your Submission Number is odd -- or during the Wednesday poster session if it is even.
- Tables, chairs, and electrical power will NOT be provided for the posters.
- You must bring your posters ready for display. The conference does not provide printing services.
