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Found 8 papers, 1 papers with code
Generative AI and Process Systems Engineering: The Next Frontier
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE).
Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning
Most of the processes for industrial mAb production rely on batch operations, which result in significant downtime.
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds.
Computing control invariant sets of nonlinear systems: decomposition and distributed computing
In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems.
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work.
Robust economic MPC of the absorption column in post-combustion carbon capture through zone tracking
To ensure that the zone tracking objective is achieved in the presence of model uncertainties and time-varying flue gas flow rate, we propose a method to modify the original target zone with a control invariant set.
Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity
In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems.
Robust economic model predictive control with zone tracking
To optimize the economic performance within the zone in the presence of disturbances, we introduce the notion of risk factor in the controller design.
