A5030631754- Advanced Control Systems Optimization
- Water Systems and Optimization
- Process Optimization and Integration
- Advanced Optimization Algorithms Research
- Optimal Power Flow Distribution
- Water Treatment and Disinfection
- Power System Optimization and Stability
- Fault Detection and Control Systems
- Probabilistic and Robust Engineering Design
- Groundwater flow and contamination studies
- Simulation Techniques and Applications
- Computational Physics and Python Applications
- Risk and Safety Analysis
- Infrastructure Resilience and Vulnerability Analysis
- Manufacturing Process and Optimization
- COVID-19 epidemiological studies
- Electric Power System Optimization
- Risk and Portfolio Optimization
- Water resources management and optimization
- Urban Stormwater Management Solutions
- Advanced Multi-Objective Optimization Algorithms
- Scheduling and Optimization Algorithms
- Water Quality Monitoring Technologies
- Metaheuristic Optimization Algorithms Research
- Matrix Theory and Algorithms
Carnegie Mellon University
2003-2025
Sandia National Laboratories
2005-2023
Sandia National Laboratories California
2017-2022
National Energy Technology Laboratory
2022
Purdue University West Lafayette
2012-2021
Center for Discrete Mathematics and Theoretical Computer Science
2017
Texas A&M University
2007-2013
Mitchell Institute
2012
University of Pittsburgh
2007
San Bernardino County Department of Public Health
1995
We formulate and solve an estimation problem for identifying both the time location of contamination sources in municipal water networks using concentration measurements from a sparse sensor grid. Previous work showed that direct sequential approach was insufficient to time-dependent problem. Instead, simultaneous is used, converging network model optimization problems simultaneously. An origin tracking algorithm presented reformulate pipe expressions characterize delays associated with...
Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic interconnected, which require new capabilities to effectively model optimize their design operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization be able rapidly incorporate advances. To address these requirements, we have developed Institute for Design Advanced Systems (IDAES) Integrated Platform, builds on strengths both process...
We propose a mathematical programming-based approach to optimize the unit commitment problem with alternating current optimal power flow (ACOPF) network constraints. This is nonconvex mixed-integer nonlinear program (MINLP) that we solve through solution technique based on outer approximation method. Our cooptimizes real and reactive scheduling dispatch subject both constraints ACOPF The proposed local method leverages powerful linear commercial solvers. demonstrate relative economic...
Abstract Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling ongoing SARS-CoV-2 pandemic. We estimated weekly values of effective basic reproductive number (R eff ) using a mechanistic metapopulation model and associated these with county-level characteristics NPIs in United States (US). Interventions that included school leisure activities closure nursing home visiting bans were all median R below 1 when combined either stay at orders (median 0.97,...
This manuscript presents a complete framework for the development and verification of physics-informed neural networks with application to alternating-current power flow (ACPF) equations. Physics-informed (PINN)s have received considerable interest within systems communities their ability harness underlying physical equations produce simple network architectures that achieve high accuracy using limited training data. The methodology developed in this work builds on existing methods explores...
This paper addresses the problem of contamination source determination in municipal drinking water networks. In previous work, authors introduced a large-scale nonlinear programming approach for identifying both time and location sources given concentration information from limited number sensors. Due to sparseness sensor grid, this inherently has nonunique solutions. The was therefore regularized, regularized solution provided an approximate linear combination possible injection scenarios....
The pooling problem is an important optimization that encountered in process operation and scheduling. Because of the presence bilinear terms, traditional formulation nonconvex. Consequently, there a need to develop computationally efficient easy-to-implement global-optimization techniques. In this paper, new approach proposed based on three concepts: linearization by discretizing nonlinear variables, preprocessing using implicit enumeration discretization form convex-hull which limits size...
We describe PyNumero, an open-source, object-oriented programming framework in Python that supports rapid development of performant parallel algorithms for structured nonlinear problems (NLP’s) using the Message Passing Interface (MPI). PyNumero provides three fundamental building blocks developing NLP algorithms: a fast interface calculating first and second derivatives with AMPL Solver Library (ASL), number interfaces to efficient linear solvers, block-structured vectors matrices based on...
Abstract Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, price renegotiation, among other factors. In context, we propose a multiperiod mixed‐integer linear programming model that integrates production, scheduling, shipping, order management minimize financial...
Abstract In less than two decades, nonlinear model predictive control has evolved from a conceptual framework to an attractive, general approach for the of constrained processes. These advances were realized both through better understanding stability and robustness properties as well improved algorithms dynamic optimization. This study focuses on recent in optimization formulations algorithms, particularly simultaneous collocation‐based approach. Here, we contrast this with competing...
We demonstrate that a strong upper bound on the objective of alternating current optimal power flow (ACOPF) problem can significantly improve effectiveness optimization-based bounds tightening (OBBT) number relaxations. additionally compare performance relaxations ACOPF problem, including rectangular form without reference bus constraints, with and polar form. find strengthen existing if constraints are included. Overall, perform best. However, neither nor dominates other. Ultimately, these...
We propose a novel global solution algorithm for the network-constrained unit commitment problem incorporating nonlinear alternating current model of transmission network, which is nonconvex mixed-integer programming (MINLP) problem. Our based on multi-tree optimization methodology, iterates between lower-bounding and upper-bounding exploit mathematical structure with AC power flow constraints (UC-AC) leverage optimization-based bounds tightening, second-order cone relaxations, piecewise...