by proposing a predictive method with no adjustable parameter and by using infinite dilution activity coefficients of components in binary mixtures obtained from unifac model, the binary interaction parameters (k12) in van der waals mixing rule (vdwmr) and orbey-sandler mixing rule (osmr) have been evaluated. the predicted binary interaction parameters are used in peng-robinson-stryjek-vera equ...

This paper highlights the capability and potential of a recently-introduced method of designing modelpredictive safety (MPS) systems (Mohseni Ahooyi et al., 2016). For the first time, the method proposed a systematic utilization of process models to generate predictive alarm signals (alerts) for the detection of present and future operation hazards (OHs) in real time. An MPS system uses a proce...

−Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction ...

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models ...

This paper presents a neighborhood based networked predictive adaptive controller that is suitable for control of multivariable systems made of subsystems that interact sequentially and on its demonstration using simulations in a detailed nonlinear model of a water delivery canal. Furthermore some convergence properties of the adaptive algorithm are proved. According to the approach followed, t...

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model st...

Model predictive control (MPC) anticipates future events to take appropriate control actions. Nonlinear MPC (NMPC) describes systems with nonlinear models and/or constraints. A Continuation/GMRES Method for NMPC, suggested by T. Ohtsuka in 2004, uses the GMRES iterative algorithm to solve a forward difference approximation Ax = b of the Continuation NMPC (CNMPC) equations on every time step. Th...

We propose a distributed model predictive control approach for linear time-invariant systems coupled via dynamics. The proposed approach uses the tube MPC concept for robustness to handle the disturbances induced by mutual interactions between subsystems; however, the main novelty here is to replace the conventional linear disturbance rejection controller with a second MPC controller, as is don...