We proposed a personalized bolus advisor for patients with type 1 diabetes (T1D). A bolus advisor is a decision support system that recommends insulin doses based on an open‐loop model‐based optimization. To construct the bolus advisor, the optimal open‐loop control of blood glucose (BG) concentration in T1D patients was represented as a multi‐objective optimization problem. The insulin types, doses, and times for each injection were provided by the bolus advisor based on a personalized model and an average daily diet, which should be re‐tuned frequently in specific time intervals. The constructed personalized model for T1D patients incorporates effects of the patient's age and body weight. Two treatment schemes using three types

Olefins production plants are large-scale processes in most of which gaseous and liquid hydrocarbons are cracked to produce light olefins. The complex and large-scale nature of these plants makes it an utmost necessity to design and operate them by using of computer-aided optimization and control methods. This review paper provides an overview of the reported research works on the optimization and control of different parts of olefin plants. The main research studies are discussed in to main sections of Optimal design, and Process operation and control. In the optimal design section, the state of the optimal design of cracking furnace systems, cold-end separation systems, and separation columns have been studied. Then in process operation a

Many process control problems with complex qualitative specifications cannot be addressed via conventional control design methods. Examples of such specifications include logic specifications expressed in the design of start-up, shut-down, changeover, and emergency shutdown operating procedures. In recent years, it has been shown in the control systems and computer science communities that symbolic models provide convenient and powerful mechanisms to synthesize controllers enforcing such qualitative specifications. The use of symbolic models reduces the synthesis of the controllers to a fixed-point computation problem over a finite-state abstract system. In this paper, after explaining the notion of approximate bisimulation for incrementall

As an integral part of a plantwide control system for large-scale nonlinear systems with non-measurable states and time-delay in measured outputs, a multi-input multi-output (MIMO) adaptive neural network predictive controller (ANNPC) is presented. A neural network model-based observer is used in the structure of the proposed controller to estimate the unknown states. Then, an adaptive predictor is designed based on the observer and is employed to predict non-measurable states. Stability of the proposed observer and controller is proved using Lyapunov function theorem. The proposed controller is used as a part of the control system of a Vinyl Acetate monomer (VAM) process. A new partially-centralized structure is developed for plantwide con

The aim of this paper is to synthesize a hybrid controller for pressure swing adsorption (PSA) processes. Since the process is described by a set of partial differential algebraic equations, first a local reduced-order model (LROM) for the process is developed and is formalized as a hybrid system. A hybrid controller is designed for purity control of the process in the presence of external disturbances by determining the maximal safe set of the LROM. A hybrid backward reachability analysis is performed for this purpose. Considering a realistic scenario for PSA processes where the states are not available and the number of measurement sensors is very limited, the desired states are estimated by using a hybrid observer. The controller is desi

In this paper we propose a method towards purity control of pressure swing adsorption (PSA) processes which is based on the use of hybrid systems formalism. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe in detail PSA processes. Based on mechanistic model of the processes, a local reduced-order model (LROM) is developed for PSA processes. Then the processes are represented as hybrid systems whose continuous evolution is described by the LROM. We then perform an analysis of hybrid reachability properties of the hybrid system obtained, based on which the so-called maximal safe set is computed. The analysis is performed for a two-bed, six-step benchmark PSA process and the influence of

A dynamical hybrid observer is proposed for online reconstruction of the active mode and continuous states of Pressure Swing Adsorption (PSA) processes as an integral part of a hybrid control system. A mode observer is designed for estimation of the active mode, and the continuous spatial profiles are estimated by a Distributed and Decentralized Switching Kalman Filter. The proposed hybrid observer has been applied, in silico, for a two-bed, six-step PSA process. The active mode of the process along with the continuous spatial profiles of its adsorption beds have been estimated quite accurately based on very limited number of noise corrupted temperature and pressure measurements.

As an integral part of a hybrid control system for Pressure Swing Adsorption (PSA) processes, a dynamical hybrid observer is proposed for online reconstruction of the active mode and continuous states of these processes. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe PSA processes. For estimation of the active mode, a mode observer is designed, and the continuous spatial profiles in each mode are estimated by distributed and decentralized Kalman filters. The proposed hybrid observer has been applied, in silico, for a two-bed, six-step PSA process used for Hydrogen purification. The active mode of the process along with the continuous spatial profiles of its adsorption beds have been

A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes. The introduced observer is an integral part of the control strategy of hybrid systems in general and PSA systems in particular. A reduced order model is developed based on the mechanistic model of the process. The sensors are optimally located and observability of the process is studied. The proposed observer is used to estimate the spatial profiles of various states of a two-bed, six-step PSA system used for production of pure H2 from a H2–CH4 gas mixture. The spatial profiles of the system have been estimated using the proposed observer quite accurately and rapid

Utilization of membrane humidifiers is one of the methods commonly used to humidify reactant gases in polymer electrolyte membrane fuel cells (PEMFC). In this study, polymeric porous membranes with different compositions were prepared to be used in a membrane humidifier module and were employed in a humidification test. Three different neural network models were developed to investigate several parameters, such as casting solution composition, membrane thickness, operating pressure, and flow rate of input dry air which have an impact on relative humidity of the exhausted air after humidification process. The three mentioned models included Feed-Forward Back-Propagation (FBP), Radial Basis Function (RBF), and Feed-Forward Genetic Algorithm (

Cyclic adsorption processes of PSA, VSA, and TSA were modeled and numerically simulated using SAPO-34 core-shell adsorbent. The results were compared with ordinary SAPO-34 to achieve a more efficient process for CO2–CH4 separation. OCM coupled with method of lines was used for numerical solution of the mechanistic model. The simulation results revealed higher efficiency of core-shell adsorbent with less usage of SAPO rather than the ordinary adsorbent to achieve the same degree of purification and recovery. VSA and TSA processes against PSA resulted in CH4 purification capability more than 99% with more than 73% recovery. However, VSA process has revealed higher productivity rather than TSA.

One of the most important issues in controller design and analysis of a nonlinear system is the degree of nonlinearity in the system. Helbig et al. proposed a method in which the system along with a linear reference model (which is the sum of a couple of rst order transfer functions) are stimulated with a set of input patterns. The outputs of these systems are then compared and the nonlinearity measure of the system is obtained as normalized di erence between the outputs of these two systems. In this paper, the linear reference model is replaced with two simpler linear models. The proposed method has been used for the assessment of the nonlinearity measure of various nonlinear systems that are used as standard benchmarks by the nonlinear pr