About

Chrysanthos Gounaris is a Professor of Chemical Engineering at Carnegie Mellon University. He received a Dipl. in Chemical Engineering in 2002 and an M.Sc. in Process Control in 2003 from the National Technical University of Athens. He then earned an M.A. in 2005 and a Ph.D. in Chemical Engineering in 2008 from Princeton University, where his doctoral thesis focused on advances in global optimization and the rational design of shape-selective separations, exploring nonlinear modeling and global optimization techniques in porous materials. After completing his Ph.D., Gounaris worked as an associate at McKinsey & Co., providing strategic and operational consulting to petrochemical, pharmaceutical, and consumer goods companies. He returned to academia for postdoctoral research at Princeton University from 2010 to 2013 before joining Carnegie Mellon University as an assistant professor in the Department of Chemical Engineering. His research interests include advanced process decision-making, AI, energy and climate, carbon management, chemical engineering, energy production, decarbonization, sustainability, industrial decarbonization and efficiency, materials science, multiscale modeling, nanomaterials, operations research, process optimization, process systems engineering, systems modeling, and transportation systems.

Research topics

• Computer Science
• Machine Learning
• Mathematical optimization
• Chemistry
• Algorithm
• Mathematics
• Environmental engineering
• Biochemical engineering
• Environmental science
• Process engineering
• Business
• Engineering
• Computer network
• Finance
• Operations research
• Theoretical computer science
• Organic chemistry

Selected publications

• Epistemic Uncertainty Analysis and Robust Optimization of a Second‐Generation Solvent‐Based Post‐Combustion Carbon Capture Process

  Engineering Reports · 2026-04-01

  articleOpen accessSenior authorCorresponding

  ABSTRACT Amine‐based carbon capture is widely regarded as a promising avenue for reducing industrial emissions, with the piperazine advanced flash stripper (PZ/AFS) process being a prominent example under active development. While process optimization can be employed to improve its economics, the many inherently uncertain parameters in its process model compromise the reliability of the resulting designs. In this work, the previously developed Pyomo‐based PZ/AFS process model is subjected to a comprehensive uncertainty analysis. The parameters that have the greatest impact on capture performance and costs are determined through sensitivity analysis, followed by a Pedigree analysis to assess knowledge strength for epistemic uncertainties. The chosen uncertainties are characterized through parameter estimation and expressed as a 12‐dimensional uncertainty set. Based on this, a robust optimization problem is solved to mitigate the risk posed by parametric uncertainties in the PZ/AFS flowsheet designs. Uncertainties in CO 2 equilibrium pressure and heat of absorption, along with the reaction rate, have the greatest impact on optimal costs, suggesting that one can benefit from additional efforts to estimate these parameters with better confidence. Compared to the deterministically optimal solution, the most substantial design changes to achieve robustness were noted for the pumps, highlighting the need for their careful design.

  PublisherDOI

• Matheuristic approaches for multi-product 3-dimensional container loading problem

  Applied Operations and Analytics · 2026-03-18

  articleOpen accessSenior author

  The problem of packing cases in three-dimensional space in a space-efficient manner is a challenging optimization problem that has wide application in supply-chain and logistics planning across various industries where physical products must be loaded and packed for storage and/or shipped to different destination locations. This work explores the use of matheuristic approaches to solve the 3D container loading problem to obtain high-quality loading plans in reasonable computation time for different levels of heterogeneity in the product mix. In our problem of interest, cuboidal cases must be arranged in a large cuboidal container, such as a shipper or gaylord box, allowing for two different case orientations around the vertical axis. We use a number of synergistic objectives to maximize space utilization while ensuring that items belonging to the same product SKU are preferentially placed together. We test and compare different solution methods, including full-space and decomposition-based ones, for placing items in 2D or 3D space. The decomposition methods use mathematical programming models in combination with embedded heuristics and construct a complete solution in three-dimensional space in an iterative manner. Results indicate that the 2D successive layer packing method is the most viable approach when considering solution quality and computational efficiency.

  PublisherDOI

• Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

  ACS ES&T Engineering · 2026-04-24

  article

  Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE. We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H2SO4 in the acidification steps. We were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

  PublisherDOI

• Simulation-Based Optimization of the Solid-Phase Peptide Synthesis Process

  Organic Process Research & Development · 2026-05-04

  articleOpen accessSenior authorCorresponding

  Synthetic peptide drugs are becoming increasingly popular in a multitude of therapeutic areas. The primary method employed to manufacture synthetic peptides at an industrial scale is the Solid-Phase Peptide Synthesis (SPPS) process, where the desired sequence of amino acids are added in series to build the peptide on solid resin. Arguably, SPPS is a complex process that involves many steps and degrees of freedom. In this work, we propose a simulation-based optimization framework to determine optimal operating variables, such as raw material charges and batch times, for any given peptide build of interest. Through the use of Python-based tools, a detailed simulation is formulated based on the reaction network for the process and used to determine key performance metrics, such as cost, throughput, and purity, for a specific operating point. This simulator is then utilized as the basis for a derivative-free optimization approach that allows us to determine optimal raw material charges and reaction times for each cycle of the peptide build. A comprehensive computational study based on a set of benchmark multicycle peptide builds showcases our framework’s ability to extract improvements in cost, throughput, and purity over standard operating protocols and manage the trade-off among all of these metrics. In general, this computational tool helps practitioners in the pharmaceutical industry quickly identify promising operating conditions for novel peptide builds that can be further validated experimentally.

  PublisherDOI

• Constructing tight quadratic relaxations for global optimization: II. underestimating difference-of-convex (D.C.) functions

  Journal of Global Optimization · 2025-12-01 · 1 citations

  preprintOpen accessSenior author

  Abstract Recent advances in the efficiency and robustness of algorithms solving convex quadratically constrained quadratic programming (QCQP) problems motivate developing techniques for creating convex quadratic relaxations that, although more expensive to compute, provide tighter bounds than their classical linear counterparts. In the first part of this two-paper series (Strahl et al. Constructing tight quadratic relaxations for global optimization: I. Outer-approximating twice-differentiable convex functions. Forthcoming, (2024)), we developed a cutting-plane algorithm to construct convex quadratic underestimators for twice-differentiable convex functions, which we extend here to address the case of non-convex difference-of-convex (d.c.) functions as well. Furthermore, we generalize our approach to consider a hierarchy of quadratic forms, thereby allowing the construction of even tighter underestimators. Utilizing a benchmark library of d.c. functions, we demonstrate noteworthy reduction in the hypervolume between our quadratic underestimators and linear ones constructed at the same points. Additionally, we construct convex QCQP relaxations at the root node of a spatial branch-and-bound tree for a set of systematically created d.c. optimization problems in up to four dimensions, and we show that our relaxations reduce the gap between the lower bound computed by the state-of-the-art global optimization solver BARON and the optimal solution by an excess of 90%, on average.

  PublisherOA PDFDOI

• Process Systems Engineering-Informed Design and Scale-Up of Multi-stage Diafiltration Cascades for Lithium and Cobalt Recovery from Spent Lithium-Ion Batteries

  2025-04-08

  reportOpen access

  Key Finding:device and systems scale challenges, not a materials problem https:/

  PublisherDOI

• Constructing Tight Quadratic Relaxations for Global Optimization: I. Outer-Approximating Twice-Differentiable Convex Functions

  Journal of Global Optimization · 2025-12-01 · 1 citations

  articleOpen accessSenior author

  Abstract When computing bounds, spatial branch-and-bound algorithms often linearly outer approximate convex relaxations for non-convex expressions in order to capitalize on the efficiency and robustness of linear programming solvers. Considering that linear outer approximations sacrifice accuracy when approximating highly nonlinear functions and recognizing the recent advancements in the efficiency and robustness of available methods to solve optimization problems with quadratic objectives and constraints, we contemplate here the construction of quadratic outer approximations of twice-differentiable convex functions for use in deterministic global optimization. To this end, we present a novel cutting-plane algorithm that determines the tightest scaling parameter, $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> , in the second-order Taylor series approximation quadratic underestimator proposed by Su et al. [25]. We use a representative set of convex functions extracted from optimization benchmark libraries to showcase–qualitatively and quantitatively–the tightness of the constructed quadratic underestimators and to demonstrate the overall computational efficiency of our algorithm. Furthermore, we extend our construction procedure to generate even tighter quadratic underestimators by allowing overestimation in infeasible polyhedral regions of optimization problems, as informed by the latter’s linear constraints.

  PublisherOA PDFDOI

• Economic Optimization and Impact of Utility Costs on the Optimal Design of Piperazine-Based Carbon Capture

  Systems and Control Transactions · 2024-07-09 · 1 citations

  articleOpen accessSenior author

  Recent advances in process design for solvent-based, post-combustion capture (PCC) processes, such as the Piperazine/Advanced Flash Stripper (PZ/AFS) process, have led to a reduction in the energy required for capture. Even though PCC processes are progressively improving in Technology Readiness Levels (TRL), with a few commercial installations, incorporating carbon capture adds cost to any operation. Hence, cost reduction will be instrumental for proliferation. The aim of this work is to improve process economics through optimization and to identify the parameters in our economic model that have the greatest impact on total cost to build and operate these systems. To that end, we investigated changes to the optimal solution and the corresponding cost of capture considering changes in the price of utilities and solvent. We found that changes in solvent price had the most effect on the cost of capture. However, re-optimizing the designs in the event of price changes did not lead to significant improvements in the case of piperazine, cooling water and electricity, whereas re-optimizing for changes in steam prices lead to yearly saving of 3.8%. These findings show that the design choices obtained at the nominal optimal solution are insensitive to utility price changes except for the case of steam and that there is a need for altered designs for locations where the steam prices are different.

  PublisherOA PDFDOI

• Process systems engineering enables efficient and sustainable membrane-based critical material separations

  2024-10-30

  articleOpen access

  Key Finding:device and systems scale challenges, not a materials problem https:/

  PublisherOA PDFDOI

• Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering

  Systems and Control Transactions · 2024-07-09 · 1 citations

  articleOpen accessSenior author

  In this work, we present recent algorithmic and implementation advances of the nonconvex two-stage robust optimization solver PyROS. Our advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the formulations and/or initializations of the various subproblems used by the underlying cutting set algorithm, and extensions to the pre-implemented uncertainty set interfaces. The effectiveness of PyROS is demonstrated through the results of an original benchmarking study on a library of over 8,500 small-scale instances, with variations in the nonlinearities, degree-of-freedom partitioning, uncertainty sets, and polynomial decision rule approximations. To demonstrate the utility of PyROS for large-scale process models, we present the results of a carbon capture case study. Overall, our results highlight the effectiveness of PyROS for obtaining robust solutions to optimization problems with uncertain equality constraints.

  PublisherOA PDFDOI

Recent grants

• UNS: Improved Risk Mitigation Strategies for Industrial Process Scheduling

  NSF · $310k · 2015–2018

• Collaborative Research: Robust Optimization of Rich Vehicle Routing Problems Under Uncertainty

  NSF · $272k · 2014–2018

• Collaborative Research: Design of Optimal Bimetallic Nanoparticles

  NSF · $200k · 2016–2020

Frequent coauthors

• Christodoulos A. Floudas

  34 shared

• Anirudh Subramanyam

  Pennsylvania State University

  13 shared

• Akang Wang

  12 shared

• Panagiotis P. Repoussis

  Athens University of Economics and Business

  9 shared

• Christopher L. Hanselman

  Carnegie Mellon University

  8 shared

• James Cheng‐Chung Wei

  Chung Shan Medical University

  7 shared

• Natalie M. Isenberg

  Pacific Northwest National Laboratory

  7 shared

• Ruth Misener

  Imperial College London

  7 shared

Labs

• Chrysanthos Gounaris LabPI

  Not provided

Education

• Other, Chemical Engineering

  National Technical University of Athens

  2002

• M.S., Process Control

  National Technical University of Athens

  2003

• M.A., Chemical Engineering

  Princeton University

  2005

• Ph.D., Chemical Engineering

  Princeton University

  2008

Awards & honors

• 2021 CMU Engineering Faculty Award winners