Browsing NTNU Open by Author "Krishnamoorthy, Dinesh"
Now showing items 1-20 of 25
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A Distributed Algorithm for Scenario-based Model Predictive Control using Primal Decomposition *
Krishnamoorthy, Dinesh; Foss, Bjarne Anton; Skogestad, Sigurd (Journal article; Peer reviewed, 2018)In this paper, we consider the decomposition of scenario-based model predictive control problem. Scenario MPC explicitly considers the concept of recourse by representing the evolution of uncertainty by a discrete scenario ... -
A Distributed Optimization Strategy for Large Scale Oil and Gas Production Systems
Krishnamoorthy, Dinesh; Aguiar, Marco Aurelio; Foss, Bjarne Anton; Skogestad, Sigurd (Journal article; Peer reviewed, 2018)n this paper, we consider the problem of real-time optimization of an oil and gas production network. The production network is often made of several wells from different reservoir sections producing to a common processing ... -
A Dynamic Extremum Seeking Scheme Applied to Gas Lift Optimization
Krishnamoorthy, Dinesh; Ryu, Jeongrim; Skogestad, Sigurd (Journal article; Peer reviewed, 2019)This paper presents the application of a data-driven optimization scheme using transient measurements to a gas-lift optimization problem. Optimal operation of a gas-lifted field involves controlling the marginal gas-oil ... -
A Primal decomposition algorithm for distributed multistage scenario model predictive control
Krishnamoorthy, Dinesh; Foss, Bjarne Anton; Skogestad, Sigurd (Journal article; Peer reviewed, 2019)This paper proposes a primal decomposition algorithm for efficient computation of multistage scenario model predictive control, where the future evolution of uncertainty is represented by a scenario tree. This often results ... -
Changing between Active Constraint Regions for Optimal Operation: Classical Advanced Control versus Model Predictive Control
Reyes-Lúa, Adriana; Zotica, Cristina Florina; Das, Tamal; Krishnamoorthy, Dinesh; Skogestad, Sigurd (Journal article; Peer reviewed, 2018)Control structures must be properly designed and implemented to maintain optimality. The two options for the supervisory control layer are Advanced Control Structures (ACS) and Model Predictive Control (MPC). To systematically ... -
Control of Steady-state Gradient of an Ammonia Reactor using Transient Measurements
Bonnowitz, Harro; Straus, Julian; Krishnamoorthy, Dinesh; Jahanshahi, Esmaeil; Skogestad, Sigurd (Journal article; Peer reviewed, 2018)This paper presents the application of a steady-state real-time optimization strategy using transient measurements to an ammonia synthesis reactor case. We apply a new method for estimating the steady-state gradient of the ... -
Coordinated Feedback-optimizing Control for Large Scale Processes - with applications of field-wide oil & gas production system
Dirza, Risvan (Doctoral theses at NTNU;2024:256, Doctoral thesis, 2024)This thesis proposes optimization strategies for large-scale process systems, such as those found in oil and gas production, characterized by diverse subprocesses and constraints. This can be achieved by utilizing real-time ... -
Data-driven Scenario Selection for Multistage Robust Model Predictive Control
Krishnamoorthy, Dinesh; Thombre, Mandar; Skogestad, Sigurd; Jäschke, Johannes (Journal article; Peer reviewed, 2018)A main assumption in many works considering multistage model predictive control (MPC) is that discrete realizations of the uncertainty are chosen a-priori and that the scenario tree is given. In this work, we focus on ... -
A distributed feedback-based online process optimization framework for optimal resource sharing
Krishnamoorthy, Dinesh (Peer reviewed; Journal article, 2021)Distributed real-time optimization (RTO) enables optimal operation of large-scale process systems with common resources shared across several clusters. Typically in distributed RTO, the different subsystems are optimized ... -
Dynamic Extremum Seeking Control Using ARX model
Ryu, Jeongrim (Master thesis, 2018)In the design of the dynamic ESC, a wave perturbation is decided to be not a sinusoidal wave but a pseudo-random binary sequence(PRBS) wave. The reason behind this is that diverse frequencies in the PRBS wave make the ARX ... -
Fast Economic Model Predictive Control for a Gas Lifted Well Network
Suwartadi, Eka; Krishnamoorthy, Dinesh; Jaeschke, Johannes (Journal article; Peer reviewed, 2018)This paper considers the optimal operation of an oil and gas production network by formulating it as an economic nonlinear model predictive control (NMPC) problem. Solving the associated nonlinear program (NLP) can be ... -
Feedback Real-Time Optimization Strategy Using a Novel Steady-state Gradient Estimate and Transient Measurements
Krishnamoorthy, Dinesh; Jahanshahi, Esmaeil; Skogestad, Sigurd (Journal article; Peer reviewed, 2019)This paper presents a new feedback real-time optimization (RTO) strategy for steady-state optimization that directly uses transient measurements. The proposed RTO scheme is based on controlling the estimated steady-state ... -
Gas-lift Optimization by Controlling Marginal Gas-Oil Ratio using Transient Measurements
Krishnamoorthy, Dinesh; Jahanshahi, Esmaeil; Skogestad, Sigurd (Journal article; Peer reviewed, 2018)This paper presents the application of a steady-state gradient control using transient measurements to a gas-lift optimization problem. Optimal operation of a gas-lifted field involves controlling the marginal gas-oil ratio ... -
Improving Scenario Decomposition for Multistage MPC using a Sensitivity-based Path-following Algorithm
Krishnamoorthy, Dinesh; Suwartadi, Eka; Foss, Bjarne Anton; Skogestad, Sigurd; Jäschke, Johannes (Journal article; Peer reviewed, 2018)This letter proposes a computationally efficient algorithm for robust multistage scenario model predictive control (MPC). In multistage scenario MPC, the evolution of uncertainty in the prediction horizon is represented ... -
Lagrangian Decomposition for Dynamic Real-Time Optimization: Applied to Production Optimization Network
Sørlie, Ingvild (Master thesis, 2017)The scope of this thesis was to investigate the use of lagrangian decomposition for a dynamic real-time optimization(RTO) problem. The decomposition was performed on a gas lifted two-well network, controlled by a nonlinear ... -
Linear Combination of Gradients as Optimal Controlled Variables
Krishnamoorthy, Dinesh; Skogestad, Sigurd (Peer reviewed; Journal article, 2020)In this paper, we show that optimal economic operation can be achieved using feedback control, by controlling the right variables that translate economic objectives into control objectives. We formulate a generic framework ... -
Novel Approaches to Online Process Optimization Under Uncertainty: Addressing the limitations of current industrial practice
Krishnamoorthy, Dinesh (Doctoral theses at NTNU;2019:301, Doctoral thesis, 2019)In the face of growing competition and increased necessity to focus on sustainability and energy efficiency, there is a clear need to optimize the day-to-day operation of many industrial processes. This involves online ... -
On combining self-optimizing control and extremum-seeking control - Applied to an ammonia reactor case study
Straus, Julian; Krishnamoorthy, Dinesh; Skogestad, Sigurd (Journal article; Peer reviewed, 2019) -
Online Process Optimization with Active Constraint Set Changes using Simple Control Structures
Krishnamoorthy, Dinesh; Skogestad, Sigurd (Journal article; Peer reviewed, 2019)The purpose of this paper is to show that online process optimization can be achieved using simple control structures without the need for solving numerical optimization problems online. In particular, we show that changes ... -
Optimal operation with changing control objectives
Bernardino, Lucas Ferreira (Doctoral theses at NTNU;2024:196, Doctoral thesis, 2024)This thesis proposes advances in control structure design, with the goal of solving a steady-state optimization problem through feedback control. When disturbances act upon the system and change the set of constraints that ...