15 research outputs found
Dynamic Optimization of Thermodynamically Rigorous Models of Multiphase Flow in Porous Subsurface Oil Reservoirs
In this paper, we consider dynamic optimization of thermal and isothermal oil
recovery processes which involve multicomponent three-phase flow in porous
media. We present thermodynamically rigorous models of these processes based on
1) conservation of mass and energy, and 2) phase equilibrium. The conservation
equations are partial differential equations. The phase equilibrium problems
that are relevant to thermal and isothermal models are called the UV and the VT
flash, and they are based on the second law of thermodynamics. We formulate
these phase equilibrium problems as optimization problems and the phase
equilibrium conditions as the corresponding first order optimality conditions.
We demonstrate that the thermal and isothermal flow models are in a
semi-explicit differential-algebraic form, and we solve the dynamic
optimization problems with a previously developed gradient-based algorithm
implemented in C/C++. We present numerical examples of optimized thermal and
isothermal oil recovery strategies and discuss the computational performance of
the dynamic optimization algorithm in these examples.Comment: 20 pages, 6 figure
Nonlinear Model Predictive Control for Disturbance Rejection in Isoenergetic-isochoric Flash Processes
Model Predictive Control Tailored to Epidemic Models
We propose a model predictive control (MPC) approach for minimising the
social distancing and quarantine measures during a pandemic while maintaining a
hard infection cap. To this end, we study the admissible and the maximal robust
positively invariant set (MRPI) of the standard SEIR compartmental model with
control inputs. Exploiting the fact that in the MRPI all restrictions can be
lifted without violating the infection cap, we choose a suitable subset of the
MRPI to define terminal constraints in our MPC routine and show that the number
of infected people decays exponentially within this set. Furthermore, under
mild assumptions we prove existence of a uniform bound on the time required to
reach this terminal region (without violating the infection cap) starting in
the admissible set. The findings are substantiated based on a numerical case
study.Comment: 14 pages, 3 figure
Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes
In type 2 diabetes (T2D) treatment, finding a safe and effective basal
insulin dose is a challenge. The dose-response is highly individual and to
ensure safety, people with T2D titrate by slowly increasing the daily insulin
dose to meet treatment targets. This titration can take months. To ease and
accelerate the process, we use short-term artificial pancreas (AP) treatment
tailored for initial titration and apply it as a diagnostic tool. Specifically,
we present a method to automatically estimate a personalized daily dose of
basal insulin from closed-loop data collected with an AP. Based on AP-data from
a stochastic simulation model, we employ the continuous-discrete extended
Kalman filter and a maximum likelihood approach to estimate parameters in a
simple dose-response model for 100 virtual people. With the identified model,
we compute a daily dose of basal insulin to meet treatment targets for each
individual. We test the personalized dose and evaluate the treatment outcomes
against clinical reference values. In the tested simulation setup, the proposed
method is feasible. However, more extensive tests will reveal whether it can be
deemed safe for clinical implementation.Comment: 6 pages, 4 figures, 1 table. Accepted for publication in Proceedings
of the 2022 61st IEEE Conference on Decision and Control (CDC
Model-based control algorithms for the quadruple tank system: An experimental comparison
We compare the performance of proportional-integral-derivative (PID) control,
linear model predictive control (LMPC), and nonlinear model predictive control
(NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the
parameters in a continuous-discrete time stochastic nonlinear model for the QTS
using a prediction-error-method based on the measured process data and a
maximum likelihood (ML) criterion. In the NMPC algorithm, we use this
identified continuous-discrete time stochastic nonlinear model. The LMPC
algorithm is based on a linearization of this nonlinear model. We tune the PID
controller using Skogestad's IMC tuning rules using a transfer function
representation of the linearized model. Norms of the observed tracking errors
and the rate of change of the manipulated variables are used to compare the
performance of the control algorithms. The LMPC and NMPC perform better than
the PID controller for a predefined time-varying setpoint trajectory. The LMPC
and NMPC algorithms have similar performance.Comment: 6 pages, 5 figures, 3 tables, to be published in Foundations of
Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC
2023). Hilton San Antonio Hill Country, San Antonio, Texa
High-performance Uncertainty Quantification in Large-scale Virtual Clinical Trials of Closed-loop Diabetes Treatment
In this paper, we propose a virtual clinical trial for assessing the
performance and identifying risks in closed-loop diabetes treatments. Virtual
clinical trials enable fast and risk-free tests of many treatment variations
for large populations of fictive patients (represented by mathematical models).
We use closed-loop Monte Carlo simulation, implemented in high-performance
software and hardware, to quantify the uncertainty in treatment performance as
well as to compare the performance in different scenarios or of different
closed-loop treatments. Our software can be used for testing a wide variety of
control strategies ranging from heuristical approaches to nonlinear model
predictive control. We present an example of a virtual clinical trial with one
million patients over 52 weeks, and we use high-performance software and
hardware to conduct the virtual trial in 1 h and 22 min.Comment: Accepted for publication in Proceedings of the 2022 American Control
Conference (ACC), 6 pages, 8 figure