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Tian Y, Pappas I, Burnak B, Katz J, Pistikopoulos EN. Simultaneous design & control of a reactive distillation system – A parametric optimization & control approach. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116232] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Pappas I, Kenefake D, Burnak B, Avraamidou S, Ganesh HS, Katz J, Diangelakis NA, Pistikopoulos EN. Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2020.620168] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.
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Chakrabarty A, Healey E, Shi D, Zavitsanou S, Doyle FJ, Dassau E. Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2020; 28:2600-2607. [PMID: 33762804 PMCID: PMC7983018 DOI: 10.1109/tcst.2019.2939122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.
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Affiliation(s)
- Ankush Chakrabarty
- Control and Dynamical Systems Group, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA
| | - Elizabeth Healey
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Dawei Shi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Stamatina Zavitsanou
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Eyal Dassau
- Corresponding author. ; Phone: +1 (617) 496-0358
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Katz J, Pistikopoulos EN. A partial multiparametric optimization strategy to improve the computational performance of model predictive control. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Burnak B, Pistikopoulos EN. Integrated process design, scheduling, and model predictive control of batch processes with closed‐loop implementation. AIChE J 2020. [DOI: 10.1002/aic.16981] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Baris Burnak
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station College Station Texas USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station College Station Texas USA
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Papathanasiou MM, Steinebach F, Morbidelli M, Mantalaris A, Pistikopoulos EN. Intelligent, model-based control towards the intensification of downstream processes. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Papathanasiou MM, Quiroga-Campano AL, Steinebach F, Elviro M, Mantalaris A, Pistikopoulos EN. Advanced model-based control strategies for the intensification of upstream and downstream processing in mAb production. Biotechnol Prog 2017; 33:966-988. [DOI: 10.1002/btpr.2483] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 03/10/2017] [Indexed: 01/17/2023]
Affiliation(s)
- Maria M. Papathanasiou
- Dept. of Chemical Engineering; Centre for Process Systems Engineering (CPSE), Imperial College London; London SW7 2AZ U.K
- Artie McFerrin Department of Chemical Engineering; Texas A&M University, College Station; TX 77843
| | - Ana L. Quiroga-Campano
- Dept. of Chemical Engineering; Centre for Process Systems Engineering (CPSE), Imperial College London; London SW7 2AZ U.K
| | - Fabian Steinebach
- Institute for Chemical and Bioengineering; ETH Zurich; olfgang-Pauli-Str. 10/HCI F 129, W Zurich CH-8093 Switzerland
| | - Montaña Elviro
- Dept. of Chemical Engineering; Centre for Process Systems Engineering (CPSE), Imperial College London; London SW7 2AZ U.K
| | - Athanasios Mantalaris
- Dept. of Chemical Engineering; Centre for Process Systems Engineering (CPSE), Imperial College London; London SW7 2AZ U.K
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Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. Processes (Basel) 2016. [DOI: 10.3390/pr4040035] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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Charitopoulos VM, Dua V. Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming. AIChE J 2016. [DOI: 10.1002/aic.15396] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Vassilis M. Charitopoulos
- Dept. of Chemical Engineering, Centre for Process Systems Engineering; University College London; Torrington Place London WC1E 7JE U.K
| | - Vivek Dua
- Dept. of Chemical Engineering, Centre for Process Systems Engineering; University College London; Torrington Place London WC1E 7JE U.K
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Papathanasiou MM, Avraamidou S, Oberdieck R, Mantalaris A, Steinebach F, Morbidelli M, Mueller-Spaeth T, Pistikopoulos EN. Advanced control strategies for the multicolumn countercurrent solvent gradient purification process. AIChE J 2016. [DOI: 10.1002/aic.15203] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Maria M. Papathanasiou
- Dept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE); Imperial College London; SW7 2AZ Lodnon U.K
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station TX 77843
| | - Styliani Avraamidou
- Dept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE); Imperial College London; SW7 2AZ Lodnon U.K
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station TX 77843
| | - Richard Oberdieck
- Dept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE); Imperial College London; SW7 2AZ Lodnon U.K
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station TX 77843
| | - Athanasios Mantalaris
- Dept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE); Imperial College London; SW7 2AZ Lodnon U.K
| | - Fabian Steinebach
- Institute for Chemical and Bioengineering; ETH Zurich; Wolfgang-Pauli-Str. 10/HCI F 129 CH-8093 Zurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and Bioengineering; ETH Zurich; Wolfgang-Pauli-Str. 10/HCI F 129 CH-8093 Zurich Switzerland
| | - Thomas Mueller-Spaeth
- Dept. of Chemistry and Applied Biosciences, ChromaCon AG; Technoparkstr. 1 CH-8005 Zurich Switzerland
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Lee JJ, Dassau E, Zisser H, Doyle FJ. Design and in silico evaluation of an intraperitoneal-subcutaneous (IP-SC) artificial pancreas. Comput Chem Eng 2014; 70:180-188. [PMID: 25267863 DOI: 10.1016/j.compchemeng.2014.02.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Prandial glucose regulation is a major challenge for the artificial pancreas using subcutaneous insulin (without a feedforward bolus) due to insulin's slow absorption-peak (50-60 min). Intraperitoneal insulin, with a fast absorption peak (20-25 min), has been suggested as an alternative to address these limitations. An artificial pancreas using intraperitoneal insulin was designed and evaluated on 100 in silico subjects and compared with two designs using subcutaneous insulin with and without a feedforward bolus, following the three meal (40-70 g-carbohydrates) evaluation protocol. The design using intraperitoneal insulin resulted in a significantly lower postprandial blood glucose peak (38 mg/dL) and longer time in the clinically accepted region (13%) compared to the design using subcutaneous insulin without a feedforward bolus and comparable results to a subcutaneous feedforward bolus design. This superior regulation with minimal user interaction may improve the quality of life for people with type 1 diabetes mellitus.
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Affiliation(s)
- Justin J Lee
- Department of Chemical Engineering, The University of California, Santa Barbara, CA 93106-5080, USA.,Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA 93105-4321, USA
| | - Eyal Dassau
- Department of Chemical Engineering, The University of California, Santa Barbara, CA 93106-5080, USA.,Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA 93105-4321, USA
| | - Howard Zisser
- Department of Chemical Engineering, The University of California, Santa Barbara, CA 93106-5080, USA.,Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA 93105-4321, USA
| | - Francis J Doyle
- Department of Chemical Engineering, The University of California, Santa Barbara, CA 93106-5080, USA.,Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA 93105-4321, USA
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Wittmann-Hohlbein M, Pistikopoulos EN. Approximate solution of mp-MILP problems using piecewise affine relaxation of bilinear terms. Comput Chem Eng 2014. [DOI: 10.1016/j.compchemeng.2013.10.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Dassau E, Zisser H, Harvey RA, Percival MW, Grosman B, Bevier W, Atlas E, Miller S, Nimri R, Jovanovic L, Doyle FJ. Clinical evaluation of a personalized artificial pancreas. Diabetes Care 2013; 36. [PMID: 23193210 PMCID: PMC3609541 DOI: 10.2337/dc12-0948] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or "brain," can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS Initial glucose values ranged from 84-251 mg/dL. Blood glucose was kept in the near-normal range (80-180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual's glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.
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Affiliation(s)
- Eyal Dassau
- Department of Chemical Engineering, University of California, Santa Barbara, California, USA
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Combined model approximation techniques and multiparametric programming for explicit nonlinear model predictive control. Comput Chem Eng 2012. [DOI: 10.1016/j.compchemeng.2012.01.009] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Domínguez LF, Narciso DA, Pistikopoulos EN. Recent advances in multiparametric nonlinear programming. Comput Chem Eng 2010. [DOI: 10.1016/j.compchemeng.2009.10.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Currie J, Wilson D. Lightweight Model Predictive Control intended for embedded applications. ACTA ACUST UNITED AC 2010. [DOI: 10.3182/20100705-3-be-2011.00046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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