Successful integration of intermittent renewable resources into the energy mix is instrumental to meet the growing global energy demand while reducing the carbon emissions. With this study, we ...propose a strategy of mixed-integer linear programming-based simultaneous design and operation to explore the techno-economic feasibility of novel energy system networks including solar photovoltaics, wind turbines, battery storage, and dense energy carriers. A multiscale energy system engineering approach is followed combining process synthesis, scheduling, and supply chain concepts to address the trade-offs between various technologies in renewable power generation and storage, as well as energy carrier production and transportation across different locations. We apply our strategy to analyze the integration of hydrogen-based dense energy carriers (DECs) produced in a high-potential region of renewable energy in Texas in tandem with local solar production and battery storage in a low-potential region in New York to minimize the levelized cost of renewable electricity. Case study results show that DECs can offer 30–50% cost reductions to local power generation and battery systems when used as clean backup fuels.
We present a systematic framework to derive model-based simultaneous strategies for the integration of scheduling and control via multiparametric programming. We develop offline maps of optimal ...scheduling actions accounting for the closed-loop dynamics of the process through a surrogate model formulation that incorporates the inherent behavior of the control scheme. The surrogate model is designed to translate the long-term scheduling decisions to time varying set points and operating modes in the time scale of the controller. The continuous and binary scheduling decisions are explicitly taken into account in the multiparametric model predictive controllers. We showcase the framework on a stand-alone three-product continuous stirred tank reactor, and two reactors operating in parallel.
•Affine approximate models have significant impact on the multiparametric solution.•Suitable error metrics provide confidence in the developed closed loop solutions.•Knowing which approximate model ...to use and when is in general non trivial.
Incorporating a high fidelity model that accurately describes a dynamical system in a model predictive control study may often lead to an intractable formulation where the use of model approximation is required. This study examines system identification, time series modeling, and linearization in the context of multiparametric model predictive control with the use of key error metrics including: (i) a novel comparison of key features of the feasible space and objective function in the optimization formulation, (ii) integral time absolute error, (iii) error distribution analysis, and (iv) step response profiles. Two examples are used as a basis for this study: a tank system which highlights the techniques used and a Continuously Stirred Tank Reactor (CSTR).
Prescriptive maintenance can improve system effectiveness and system safety via integrated production and maintenance optimization. However due to system disruptions there is potential for abnormal ...operations and an undesirable increased occurrence of process safety incidents. This research provides a multiparametric‐based framework for safety‐aware, maintenance‐aware, and disruption‐aware process control. It leverages ensemble classification via machine learning classifiers for fault detection, mixed‐integer nonlinear programming for integrated safety‐aware production and maintenance scheduling, as well as hybrid multiparametric model predictive control for fault‐tolerant setpoint tracking. The results show that the ensemble classifier outperforms the individual classifiers in terms of fault detection accuracy, sensitivity, and specificity. Furthermore, it is seen that the developed controllers are able to reconfigure the control actions based on process disruption information. The framework is illustrated with a chemical complex system, and a cooling water system. The approach can be used to help improve the safety and productivity of industrial processes.
POP – Parametric Optimization Toolbox Oberdieck, Richard; Diangelakis, Nikolaos A; Papathanasiou, Maria M ...
Industrial & engineering chemistry research,
08/2016, Letnik:
55, Številka:
33
Journal Article
Recenzirano
In this paper, we describe POP, a MATLAB toolbox for parametric optimization. It features (a) efficient implementations of multiparametric programming problem solvers for multiparametric linear and ...quadratic programming problems and their mixed-integer counter-parts, (b) a versatile problem generator capable of creating random multiparametric programming problems of arbitrary size, and (c) a comprehensive library of multiparametric programming test problems featuring benchmark test sets for multiparametric linear, quadratic, mixed-integer linear, and mixed-integer quadratic programming problems. In addition, POP is equipped with a graphical user interface which enables the user-friendly use of all functionalities of POP and a link to the solvers of the Multi-Parametric Toolbox (MPT), as well as the ability to design explicit MPC problems. These features are demonstrated in detailed computational studies providing insights into the versatility and applicability of POP. Additionally, the example of a periodic chromatographic system is used to show the scalability of multiparametric programming in general and POP, in particular.
We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features ...(i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) data-driven regression models for fault magnitude estimation via the random forest algorithm, and (iv) a parametric optimization and control (PAROC) framework for the design of the explicit/multiparametric model predictive controller. The resulting explicit control strategies correspond to affine functions of the system states and the magnitude of the detected fault. A semibatch process, an example for penicillin production, is presented to demonstrate how the proposed framework ensures smart operation for which rapid switches between a priori computed explicit control action strategies are enabled by continuous process monitoring information.
•Multi-parametric model predictive control (mp-MPC) for hypnosis during anesthesia.•Combined control design of mp-MPC and online parameter estimation.•Evaluation of control strategy for uncertainty ...originated by patient variability.•Controller's dynamics are adjusted to the individual patient's sensitivity.•Closed loop control validation for induction and disturbance rejection.
This paper addresses inter- and intra-patient variability in the context of automated drug delivery during anesthesia. A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability. The parameter with the highest sensitivity, C50 the effect site concentration at 50% drug effect, is estimated online. The performance of the closed loop control design is shown for induction and maintenance of volatile anesthesia. In the maintenance phase, the control strategy is evaluated for predefined disturbances that are commonly occurring during surgery. The presented approach shows an improved performance compared to the nominal MPC controller under uncertainty.
Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as ...endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Design and validation of a control scheme for the semi-continuous MCSGP separation process, based on mp-MPC techniques.•Use of the integral concentrations as outputs in the control problem, creating ...a solid basis for continuous control throughout the process cycle, enabling easier online measurements.•The control scheme inherently suggests a periodic input profile that could allow cyclic steady state to be achieved.•Good agreement between the computational (control) results and the experimentally optimized profiles as provided by ETHZ.
Aiming to significantly improve their processes and secure market share, monoclonal antibody (mAb) manufacturers seek innovative solutions that will yield improved production profiles. In that space, continuous manufacturing has been gaining increasing interest, promising more stable processes with lower operating costs. However, challenges in the operation and control of such processes arise mainly from the lack of appropriate process analytics tools that will provide the required measurements to guarantee product quality. Here we demonstrate a Process Systems Engineering approach for the design a novel control scheme for a semi-continuous purification process. The controllers are designed employing multi-parametric Model Predictive Control (mp-MPC) strategies and the successfully manage to: (a) follow the system periodicity, (b) respond to measured disturbances and (c) result in satisfactory yield and product purity. The proposed strategy is also compared to experimentally optimized profiles, yielding a satisfactory agreement.
•A nominal hybrid explicit/multiparametric MPC structure was developed based on a piece-wise affine approximation of the intravenous anaesthesia model, which efficiently addresses the nonlinearity of ...the Hill curve.•Simultaneous hybrid mp-MPC and multiparametric moving horizon strategy was developed and implemented for the intravenous anaesthesia process.•Robust hybrid mp-MPC strategies were developed and simultaneously implemented and tested for the intravenous anaesthesia process.•The developed strategies successfully address two of the main challenges in the control of the intravenous depth of anaesthesia: nonlinearity and inter-and intra- patient variability.
In this work, we first present a piece-wise affine model for intravenous anaesthesia, based on which a hybrid explicit/multiparametric model predictive control strategy is developed. To deal with the inter- and intra-patient variability, an estimation strategy, the multiparametric moving horizon estimator, and different robust algorithms such as Offset Correction, State-Output Correction and Prediction Output Correction are further designed and implemented simultaneously with the hybrid multiparametric model predictive control. Simulation results for a set of 12 virtually generated patients for the regulation of the depth of anaesthesia by means of the Bispectral Index with Propofol as the anaesthetic, demonstrate the validity and usefulness of the proposed advanced control and estimation strategies.
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