About

Warren D. Seider has taught and conducted research in Chemical and Biomolecular Engineering at the University of Pennsylvania for 56 years, since 1967. He has authored and co-authored a total of 165 journal articles and co-authored seven textbooks. For over 40 years, he has been a coordinator of the design project course involving projects provided by many practicing engineers in the Philadelphia area. In 1969, he helped to organize the CACHE (Computer Aids for Chemical Engineering Education) Corporation, served as its chairman on two occasions, and remains active through 44 years. He and his students have been involved in research to develop the ASPEN (Advanced System for Process Engineering) simulator and multimedia materials to help teach the use of the ASPEN simulator. His research interests include process analysis, simulation, design, and control, with specific emphasis on phase and chemical equilibria, chemical reaction systems, integration of stiff differential equations, azeotropic distillation, heat and power integration, CO2 supercritical extraction, Czochralski crystallization, algae growth to biofuels and bioproducts, nonlinear analysis and control, polymerization reactor control, safety and risk analysis, microfluidics control, and electrical power generation and CO2 sequestration.

Research topics

• Biology
• Chemistry
• Pulp and paper industry
• Organic chemistry
• Engineering
• Computer Science
• Botany
• Artificial Intelligence
• Environmental science
• Operations management
• Mathematics
• Mathematical optimization
• Food science
• Algorithm
• Biotechnology
• Agronomy
• Chromatography
• Biochemistry

Selected publications

• Improved algebraic, numerical, and graphical representations in fluid mechanics

  AIChE Journal · 2025-11-18

  article

  Abstract The concept of equivalents is applied to fully developed flows through straight channels and two applications are introduced: the equivalents that produce equal rates of flow through channels of different cross‐sections, and the diminishment that occurs between the regimes of laminar and turbulent flow. This diminishment (i.e., the reduction of flow due to turbulence) is a counter‐intuitive finding, but it appears naturally when the flow equations in the two flow regimes are scaled appropriately for the same pressure gradient. The validity of the eddy diffusivity and the invalidity of the mixing‐length as measures of the shear stress due to turbulence have perhaps never before been demonstrated so clearly. The possible choices of primary variables and the consequences are also examined. The objective of these explorations is an improved understanding of the basic fluid mechanics by teachers, students, and industrial practitioners.

  PublisherDOI

• Teaching chemical product design

  Education for Chemical Engineers · 2025-06-02

  articleOpen access1st authorCorresponding

  The CACHE Design Task Force has conducted a comprehensive, year-long study on the teaching of chemical product design across global chemical engineering programs. This paper reviews existing literature and highlights distinctions between product and process design, emphasizing the predominance of process design education in universities. Drawing from co-author contributions and responses to a widely distributed questionnaire, we present recent teaching methodologies for chemical product design. The paper discusses trends in chemical engineering diversification and the gradual inclusion of diverse applications in curricula. It concludes with a call to action for chemical engineering educators to integrate well-established product design strategies into undergraduate programs and reflects on insights shared during the 2024 FOCAPD Conference. • Results of a 1-year study of the teaching of chemical product design worldwide. • Distinctions between teaching product and process design. • Current practice and motivation to teach process design at most universities. • Industrial practices in carrying out product design. • Challenges of merging product design strategies into the ChE undergraduate curriculum.

  PublisherDOI

• Artificial Intelligence‐Empowered Automated Double Emulsion Droplet Library Generation

  Small · 2025-03-25 · 13 citations

  articleOpen access

  Double emulsions with core-shell structures are versatile materials used in applications such as cell culture, drug delivery, and materials synthesis. A droplet library with precisely controlled dimensions and properties would streamline screening and optimization for specific applications. While microfluidic droplet generation offers high precision, it is typically labor-intensive and sensitive to disturbances, requiring continuous operator intervention. To address these limitations, we present an artificial intelligence (AI)-empowered automated double emulsion droplet library generator. This system integrates a convolutional neural network (CNN)-based object detection model, decision-making, and feedback control algorithms to automate droplet generation and collection. The system monitors droplet generation every 171 ms-faster than a Formula 1 driver's reaction time-ensuring rapid response to disturbances and consistent production of single-core double emulsions. It autonomously generates libraries of 25 distinct monodisperse droplets with user-defined properties. This automation reduces labor and waste, enhances precision, and supports rapid and reliable droplet library generation. We anticipate that this platform will accelerate discovery and optimization in biomedical, biological, and materials research.

  PublisherOA PDFDOI

• Artificial Intelligence-Empowered Automated Double Emulsion Droplet Library Generation

  ChemRxiv · 2025-01-29

  preprintOpen access

  Double emulsions, with core-shell structures, are versatile materials used in diverse applications such as cell culture, drug delivery, and materials synthesis. A library of double emulsions with precisely controlled dimensions and properties would streamline the process of screening and optimization for specific applications. Microfluidic droplet generation offers precise control over droplet dimensions and properties, making it ideal for the preparation of droplet libraries; however, their preparation is tedious because fluid flow control and emulsion collection are typically performed manually and microfluidic devices are vulnerable to minor disturbances, requiring continuous intervention by skilled operators. To address these challenges, we present an artificial intelligence (AI)-empowered automated double emulsion droplet library generator. Leveraging a convolutional neural network (CNN)-based object detection model fine-tuned on a custom dataset, the system integrates decision-making and feedback control for automated droplet generation and collection. The system monitors droplet generation every 171 ms —faster than the reaction time of Formula 1 drivers —ensuring rapid response to disruptions and consistent production of single-core double emulsions. The library generator autonomously generates libraries consisting of 25 distinct monodisperse droplets with user-specified properties. This system significantly improves droplet-based experiments by reducing labor and waste, improving precision, and supporting rapid, reliable droplet library generation.

  PublisherOA PDFDOI

• The Current Status of Process and Product Design � What and How to Teach � and a Vision for the Future

  Systems and Control Transactions · 2024-07-09

  articleOpen access1st authorCorresponding

  Process design is a core component of chemical en-gineering education and either involves or is followed by an extensive design project in most schools. The design project is often considered a core activity in the educa-tion of future chemical engineers because it develops their skills in creative and critical thinking beyond the boundaries of their acquired knowledge, as well as training them in teamwork. Such skills are likely to be crucial to empower students to develop process technologies that respond to the relevant future challenges in process design. These future challenges include accommodating alternative raw materials and energy resources, addressing sustainability concerns, and arranging production schedules that are more flexible... (ABSTRACT ABBREVIATED)

  PublisherOA PDFDOI

• Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes

  arXiv (Cornell University) · 2024-08-31

  preprintOpen accessSenior author

  Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.

  PublisherOA PDFDOI

• Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processes

  Computers & Chemical Engineering · 2024-11-15 · 7 citations

  articleSenior authorCorresponding
  PublisherDOI

• Convolutional Neural Network Augmented Soft-Sensor for Autonomous Microfluidic Production of Uniform Bubbles

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Thermoeconomic analysis of sCO₂ power cycles

  AIChE Journal · 2024-06-05 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract The second‐law analysis evaluates the irreversibilities of a process. Systematic study of the relationship between thermodynamic efficiency and process modifications enhances process synthesis. The Allam cycle is an oxy‐fuel combustion cycle with nearly complete carbon capture that offers greater efficiency than current electricity generating systems. This study applies lost work analysis to the original Allam cycle and three modifications to obtain the distribution of irreversibilities and the effects of different configurations among potential process improvements for more sustainable power generation. The major inefficiencies are from the combustors and heat exchangers. We also examine the economic profitability of the alternatives. The largest equipment costs are for the turbines, compressors, and recuperators. We find that improving efficiency leads to less economic return; a configuration with partial compression has the highest efficiency, while the original Allam cycle has the highest profitability. We discuss how to resolve this apparent conflict between sustainability and profitability.

  PublisherOA PDFDOI

• Alarm rationalization and dynamic risk analyses for rare abnormal events

  Computers & Chemical Engineering · 2024-02-16 · 12 citations

  articleCorresponding
  PublisherDOI

Recent grants

• Path Sampling and Dynamic Risk Analysis

  NSF · $350k · 2022–2027

• GOALI: Collaborative Research: Model-Predictive Safety Systems for Predictive Detection of Operation Hazards

  NSF · $108k · 2017–2021

• Dynamic Risk Assessment of Inherently Safe Chemical Processes: Using Accident Precursor Data

  NSF · $318k · 2006–2009

• EAGER: GOALI: REAL-D Path-Sampling Algorithms to Understand Rare Safety Events and Improve Alarm Systems

  NSF · $150k · 2018–2022

• Collaborative Research: GOALI: Synergistic Improvement of Process Safety and Product Quality Using Process Databases

  NSF · $360k · 2011–2014

Frequent coauthors

• Masoud Soroush

  Drexel University

  36 shared

• Lisa Bullard

  North Carolina State University

  30 shared

• Margot Vigeant

  25 shared

• David Silverstein

  University of Kentucky

  25 shared

• Ulku Oktem

  25 shared

• Jeffrey E. Arbogast

  17 shared

• Joshua M. Kanter

  University of Massachusetts Chan Medical School

  13 shared

• Soemantri Widagdo

  12 shared

Labs

• Product and Process Systems Research GroupPI

Education

• Ph.D., Chemical Engineering

  University of California, Berkeley

  1992

• M.S., Chemical Engineering

  University of California, Berkeley

  1988

• B.S., Chemical Engineering

  University of California, Berkeley

  1986

Awards & honors

• 2023 AIChE Founders Award
• 2022 Elected Fellow of American Association for the Advancem…
• 2011 AIChE F.J. Van Antwerpen Award
• 2008 AIChE – Since 1908, one of 30 Authors of Groundbreaking…
• 2005 Elected Fellow of AIChE