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The purpose of this paper is to present a state variable model for the drying section of a paper-making machine. This model is based on the mass and energy balance relationships written for steam, paper, cylindrical heater wall, and moisture. These relationships give a set of six nonlinear partial derivative equations. The form of these equations changes somewhat from one cylindrical heater to another. The solution for this model yields the operating parameters needed to achieve the desired steam temperature while obtaining good paper drying. Boundary conditions are specified by a stochastic generator, and initial conditions are obtained by solving the static model. The dynamic model is presented in [1]. In this paper, we present only the state variable model for the paper drying, as well as comparing the obtained results with dynamic model results. Solving the set of differential equations with respect to the boundary conditions, we obtain the standard form of the state representation, which represents the dynamic version of the model used for process control. In conclusion, it can be said that the consistency of the model with experimental results seems to be reasonably good and can be used for process control. Index Terms - Drying paper, linearization, state equations, steam dryer.

In this paper, the influence of the stiffness and damping of real elastic shafts on the behavior of a multidrive system driving a tissue paper machine is investigated. The drive system consists of four dc motors, where two of them drive the Yankee drying cylinder and the other two drive the presses section. Six-pulse thyristor bridge power converters that are connected to the same power transformer supply the motors. The inertias of the motors and the driven machines, in conjunction with the shaft stiffness, form mechanical resonators. Thus, resonance phenomena may occur, which may lead to undesirable results. This particular case results to two three-mass and one five-mass systems. This paper presents an analysis of the resonance frequencies of two sections of the machine during startup and machine production, considering the shaft stiffness (shaft diameter, length, and thickness). The elastic shafts eliminate the ripple of the motor electromagnetic torque, which is transferred to the driven machine. The behavior of elastic shafts with different stiffness in the transmission system is studied. Moreover, the steady state and dynamic behavior of the system considering the influence of the elastic shafts and vibration analysis were investigated using simulation. A model algorithm for system simulation was developed using the MATLAB/SIMULINK software. Characteristic simulation results are presented and are expressed in terms of speed and torque response. Considering the resonance frequency analysis and simulation results, mechanical design guidelines can be given for the most significant drive components in order to avoid torsional oscillation resonance phenomena and minimize torsional oscillations of the drive system. Index Terms--Current and torque control, dc motor drives, modeling, pulp and paper industry, resonance, simulation, vibrations.

In this paper, a novel repetitive control scheme is presented and discussed, based on the so-called B-spline filters. These dynamic filters are able to generate a B-spline trajectory if they are fed with the sequence of control points defining the curve. Therefore, they are ideal tools for generating online reference signals with the prescribed level of smoothness for driving dynamic systems, possibly together with a feedforward compensator. In particular, a continuous zero phase error tracking controller (ZPETC) can be used for tracking control of nonminimum phase systems but because of its open-loop nature it cannot guarantee the robustness with respect to modeling errors and exogenous disturbances. For this reason, ZPETC and trajectory generator have been embedded in a repetitive control scheme that allows to nullify interpolation errors even in nonideal conditions, provided that the desired reference trajectory and the disturbances are periodic. This paper is based on the results presented in the conference paper [L. Biagiotti, F. Califano, and C. Melchiorri, “Repetitive control of non-minimum phase systems along b-spline trajectories,” in Proc. IEEE 55th Conf. Decis. Control, 2016, pp. 5496–5501.], where asymptotic stability of the overall control scheme has been proved mathematically, but extends such results with an experimental validation based on a nonminimum phase system. Different models of the same physical system have been identified and used in the implementation of this model-based control scheme, allowing a real evaluation of the relationship between control system performance and model accuracy. [ABSTRACT FROM AUTHOR]

Presents IES society award recipients and distinguished reviewers for 2021. [ABSTRACT FROM AUTHOR]

When adequately maintained, the mechanical system of rewinders and similar machines in paper industry may have a significantly longer service life than its electrical and control systems. Over the lifetime of the facility, a rewinder may experience several major maintenance cycles, and these generally include significant upgrades to the electrical drive and control system. These upgrades present an opportunity to modify the design of the control system to eliminate some of the sensors or to provide signal redundancy by using state estimation. During such major reconstruction of a paper-board rewinder, it was noticed that the shipping-roll radius and the web tension force can be accurately estimated from the measurements available within the drive's control system. This paper presents the implementation of the two estimators on a commercial production rewinder. Test results from the equipment in service show that the measured signals can be supplemented with the estimated ones to provide an alternative in the event of sensor malfunction or to enable trending of accuracy of calibration. Both applications have an entitlement to reduce the downtime of the rewinder, as the replacement of the faulted sensor can be scheduled for the next planned idle period of the machine. [ABSTRACT FROM AUTHOR]

By combining radio-frequency identification (RFID) and paper-microfluidics technologies, a low-cost first-of-its-kind platform for comprehensive liquid sensing, i.e., the “smart test strip,” is presented, which enables portable wireless real-time liquid sensing with handhold devices (e.g., cell phones), and integration of various multifunctional electrical and chemical sensors, for numerous Lab-on-Chip applications, including manufacturing control, environmental monitoring, and point-of-care medical diagnostics. The fabrication of RFID tags and two types of microfluidics are accomplished by a single inkjet-printing process in a cost-effective environmental-friendly additive manufacturing approach, which makes possible the production of disposable, lightweight, and flexible sensing platforms. Taking advantage of the proposed smart test strips platforms, we demonstrate two proof-of-concept high-performance electrical sensors based on interdigitated electrode topologies: a resistivity-based sensor with a 1782 $\Omega$/( $\Omega$*m) sensitivity; nevertheless, the proposed permittivity-based sensor with a 15%/$\epsilon _r$ sensitivity, but the proposed integrated wireless platform, can facilitate the integration of even more chemical and electrical sensors. In addition, two on-strip antenna prototypes have been designed, optimized, and tested to work at 2.4 GHz and 13.56 MHz, respectively. Furthermore, the wireless interrogation of a complete proof-of-concept smart test strip is presented, which shows an excellent sensing resolution of 1.33 $\Omega$ over the range of 0–1371 $\Omega$. [ABSTRACT FROM PUBLISHER]

The article discusses various topics published within the issue including one about the development of soft computing, another on the concept of variable-structure control for uniform ultimate boundedness control and one about the power generation control in wind turbines.

An introduction is presented in which the author discusses topics in the issue including permanent-magnet motors, brushless direct current (dc) motors, and fault tolerance in the electrical drives of aircraft.

The article discusses various topics published within the issue including one by R. R. Moghaddam and colleagues on the theoretical and experimental reevaluation of synchronous reluctance machine, one by J. Smajic and colleagues on the coupling effects between electromagnetic and thermal phenomena, and one by M. I. Masoud and colleagues on the theoretical aspects of special machines.

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant. [ABSTRACT FROM AUTHOR]

The rising cost of production testing for a system-on-a-chip (SoC) is one of the crucial matters to chip makers, due to long test time and costly automated-test-equipment. This paper proposes a spectral leakage-driven built-in self-test (BIST) scheme to precisely predict the nonlinearity of mixed-signal circuits in the loopback mode, thereby accomplishing cost-effectiveness (compared to previous BIST-based works). A digitally synthesized single-tone sinusoidal stimulus used for conventional harmonic testing is incoherently sampled by a device under test (DUT). The DUT output signal exhibits the correlation between the DUT harmonics and the spectral leakage introduced by the incoherent sampling. The DUT output signal is then fed to another DUT through a loopback path, so that the harmonics of a pair of DUTs are correlated with the spectral leakage on the loopback response; the magnitude of the spectral leakage is considered as a weighting factor on the harmonic magnitude of those DUTs. The correlation is quantitatively modeled as characteristic equations in , and postprocessing predicts the harmonics of the two individual DUTs, by simultaneously solving the characteristic equations using on-chip DSP core available in an SoC. Simulation and hardware measurements validated that this paper can be practically used for production testing by showing less than 0.3 and 0.6 dB of the prediction errors, respectively. [ABSTRACT FROM AUTHOR]

Large portions of a winder's control system software are tested during commissioning. This is inefficient because production is stopped and financial pressures limit the time available for testing. However, it avoids the cost of integrating the winder and electrical drive system before installation. This paper describes a winder simulator that allowed control system software to be tested before installation. Shipping roll build up, parent roll build down, mechanical brakes, and a two-drum windup model were developed. These models were used to simulate the winder, web, and drive system dynamics in real time with a dedicated programmable logic controller (PLC). This PLC provided signals for the winder PLC that would normally be supplied by the drive system and various sensors. The simulator was verified with data from a previously installed winder. The use of the simulator before installation of a new winder demonstrated the benefits of pre-installation software testing. Index Terms--Industrial control, pulp and paper industry, real-time systems, simulation, software testing.

The fourteen papers in this special section focus on high performance light emitting diode (LED) drivers. In recent years, as the fourth generation of light source, light-emitting diodes (LEDs) have been widely used in many applications such as street lighting, automotive, and residential lighting because of their long lifetime, low maintenance requirement, and environment friendliness. As the performance of LEDs will be influenced obviously by the drivers, researchers have shown a growing interest in optimizing the drivers. Although these problems such as current balancing, eliminating electrolytic capacitor, and improving the topologies are not new, the high-frequency high-efficiency trend of LED drivers has given rise to additional challenges. These papers provide an insight on some of the newly emerging challenges and potential solutions to overcome those issues. [ABSTRACT FROM PUBLISHER]

An introduction to the special section on industrial applications and implementation of Kalman filter (KF) is presented in which the authors discuss various reports within the issue on topics including sensorless control of electrical machines, power electronics, and data processing.

The article discusses various papers published within the issue including one by N. Kubota and K. Nishida on natural interface using pointing behavior for human-robot gestural interaction and another one by G. Ogawa and colleagues on onboard evolutionary risk recognition system for automobiles.

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

This special section on "Diagnosis and Prognosis for Complicated Industrial Systems?Part II? is the continuation of its previous part I (IEEE Transactions on Industrial Electronics, Vol. 63, Issue 4, 2016). The diagnosis and prognosis approaches can be roughly classified into two categories: 1) model-based and 2) data-driven approaches. The model-based approaches perform the diagnosis and prognosis relying on the physical models from first principles, whereas the data-driven approaches make full use of the process data to monitor the systems under consideration. Due to the increasing complexity of the industrial system, new challenges are encountered in diagnosis and prognosis of such complicated systems, and more efficient process monitoring approaches are needed. From this point of view, the papers included in this section are focused on the recent developments of diagnosis and prognosis for complicated industrial systems, providing novel ideas and referential directions for both the academic and industrial communities. [ABSTRACT FROM PUBLISHER]