Set Point Value Research Articles

Analysis of the Hybridization of PV Plants with a BESS for Annual Constant Power Operation

The electrical generation of photovoltaic systems is variable and non-dispatchable. Energy storage systems can provide the system with energy management capabilities. In particular, with a hybrid system that combines a photovoltaic system and an energy storage system, it is possible to deliver firm power to the grid, if it is correctly dimensioned and operated. The objective of this work is to study the most appropriate relationship between the capacity of the battery energy storage system (BESS) and the peak power of the photovoltaic generator that enables the delivery of constant power throughout the year. Analysis parameters are presented that help to decide the most convenient energy injection constant value (PV-CPG set point) and the size of the storage system. As a case study, the most suitable battery capacity for a 1 MWp photovoltaic system with a battery located in Zaragoza (Spain), and the most convenient annual setpoint values for its operation are analyzed.

Desain Sistem Kontrol Level Cairan Dengan Metode PID Berbasis PLC

This article discusses the water level control of the tank using PID control which is implemented on PLC (Programmable Logic Control) with HMI interface. This plant system consists of a controlled tank, a sup-ply tank, a water level sensor, a water faucet in the form of a motorized valve, PLC and HMI. In previous studies, the plant system used usually only has a controlled tank without a supply tank so that the control of the system becomes simpler. However, in this study a supply tank was also used so that the flow of water flowing in controlling the water level would depend on the water level of the supply tank and the opening of the water faucet. This plant system with a tank that has an open outlet pipe is a classic system that is non-linear so linearization is needed in its control. In order for the system to be linearized, it is assumed that both the supply tank and the controlled tank only operate within a certain value range. The realiza-tion of a water level control system using a PID controller was made and tested for its effectiveness at the setpoint values of 10 cm and 20 cm. Based on the simulation and experimental results, it was found that the system worked well where the PID control was able to stabilize the system at both setpoint values where the obtained overshoot were below 10%.

Sliding-Mode Boundary Control of an In-Line Heating System Governed by Coupled PDE/ODE Dynamics

This present article addresses the feedback control design problem of in-line liquid flow heating units used to process silicon wafers in the semiconductor industry. A physics-based model is developed to describe the thermal behavior of the heater and the liquid circulating in it. Under sensible approximations, process dynamics are shown to be governed by a coupled partial differential equation (PDE)/ordinary differential equation (ODE) system. A boundary control law capable of steering the fluid temperature to the desired set-point value while rejecting matched disturbances is developed. The proposed boundary control is sliding-mode-based and makes use of boundary measurements only. The convergence properties of the closed-loop system are formally demonstrated by means of Lyapunov-based analysis. In addition, the input-to-state stability (ISS) properties of the closed-loop system with respect to nonmatching boundary disturbances are investigated. To corroborate the theoretical findings, this article additionally presents the experimental results with the proposed controller implemented in the closed loop on a real in-line heating unit.

Deployment of metabolic heat rate based soft sensor for estimation and control of specific growth rate in glycoengineered Pichia pastoris for human interferon alpha 2b production

Lack of appropriate process models, reliable online sensors, and process variability in bioprocess systems are poising challenges in real-time monitoring and control of critical process parameters (CPPs). This present investigation deals with the development of a non-invasive soft sensor by utilizing metabolic heat rate as input signal for online estimation of specific growth rate (μest) during the induction phase of glycoengineered Pichia pastoris for human interferon-alpha 2b (huIFNα2b) production. Feedforward strategy employing a predetermined exponential feeding of methanol during the induction phase was dealt at defined setpoint values (μSP). Standard PID controller with predetermined gain values regulated methanol feeding in accordance with the deviation from the μSP value. An adaptive PID (gain scheduling) significantly minimized the deviation of μ from its μSP value, reduced the amplitude of oscillation and achieved long-term controller stability. Robust control of methanol feeding by adaptive PID resulted in a 1.5 and 2.2-fold increase in productivity of huIFNα2b compared to standard PID and feedforward controls respectively. Moreover, adaptive PID control facilitated narrow range control of μ for longer durations (> 20 h) with a low average tracking error (< 6%) enumerating its scope of application in therapeutic protein production in near future.

Pressure management in water distribution systems through PRVs optimal placement and settings

Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network.

Auto-Tuning PI Controller Design Using Fuzzy Logic Controller for Web Tension Control in Roll Rewinder System

Paper has a very important role, because it increases the productivity of pulp and paper companies in Indonesia. The company's development in 2018 increased by 2.1%, this was indicated by increasing consumer demand. Therefore, paper becomes a necessity in everyday life. The criteria for the paper to be produced are paper with a flat surface and cannot be wrinkled. However, errors in paper production that often occur are a lot of wrinkles and uneven paper surfaces. This happens because of a discrepancy in the dynamics of paper tension with the standards that have been set. The paper record is caused by the dynamics of the web tension in the Rewinder Roll system. In order for the web tension to remain stable, a controller is needed. The controller used is Auto-Tuning PI controller using Fuzzy Logic Controller (FLC). The controller can be said to be successful when it is able to perform automatic tuning of setpoint changes. The results showed that the system was able to achieve setpoint values, overcome overshoot and steady-state error (Ess) with response time values, namely Tr=0.3151 seconds, Ts=0.3750, Td=0.0721, overshoot=0.0044%, and Ess=0 N.

Induction Motor Torque Measurement using Prony Brake System and Close-loop Speed Control

Three-phase induction motors are the main drivers of the industrial world because of their low price and good reliability. However, this type of motor does not have built-in speed control. These problems can be overcome by utilizing the Variable Frequency Drive (VFD) inverter. This research investigates the induction motor's characteristics in every load condition and combines a VFD inverter with an external speed controller based on Arduino. The motor is mounted on a Prony brake testbed frame to measure the motor's torque and mechanical power. The test results show the highest torque value obtained is 0.57 Nm, and the highest output power value is 0.042 kW. The motor cannot maintain the setpoint speed after loading in the open-loop control system. Meanwhile, the closed-loop control system has been successfully implemented, and the motor can return the speed to the setpoint value after loading, with an average settling time of 14.67 seconds.

MPPT algorithm based on Multiple Linear Regression model for Solar PV systems

Currently, there are numerous maximum power point tracking (mppt) algorithms implemented in systems powered by photovoltaic (PV) panels. These systems are indirectly affected by sudden changes in weather conditions, making them unstable and inefficient. This work presents a model to establish the optimal working point of photovoltaic panels to be used in installations sensitive to changes in environmental conditions, such as solar pumping, to obtain maximum power in any temperature and irradiance condition of the panels. The model reduces the response time of the system because it also has a control system implemented whose setpoint value is the one provided by the algorithm itself. This is possible with the help of external temperature and solar radiation sensors that provide these values in real time. The setpoint is the working voltage target for the panels (Vmpp), for which the system does not become unstable despite the oscillations allowed by the control system. The algorithm is based on a model that consists of a Multiple Linear Regression through which the entire P-V curve of any photovoltaic panel can be represented for any temperature and radiation. The characteristics of the model allow a control system design that makes the system more efficient.

Subsurface phase imaging of tapping-mode atomic force microscopy at phase resonance.

The phase image of tapping-mode atomic force microscopy (TM-AFM) contains energy dissipation, which is related to the sample information on the physical properties such as the sample Young's modulus, adhesion, surface morphology and subsurface morphology. When TM-AFM is used for sample measurement, the frequency near the first resonance peak of probe is usually selected to drive the probe vibration. When the probe vibration is driven by the frequency, the probe has a high amplitude sensitivity, but the phase sensitivity is relatively low. In this paper, the frequency at the probe phase resonance peak was selected for driving the probe vibration to measure the sample, which improved the image resolution. Phase imaging was performed on three uniform photoresist samples with different thicknesses and the same structure. When the scanning parameters were fixed and the probe setpoint value was changed alone, it was found that with the decrease of setpoint value the horizontal resolution of the phase subsurface image was decreased, and the depth sensitivity was increased first and then decreased. The result shows that TM-AFM working at the phase resonance peak can better realise the subsurface imaging of samples at different depths. Phase subsurface imaging at the resonance can be used to quantitatively obtain subsurface phase images of different depths.

Numerical analysis of the performance of a PID control based real-time mechanical ventilation system to prevent smoke back-layering in tunnel fires

Longitudinal mechanical ventilation systems are widely employed to prevent smoke back-layering in tunnel fires. Correlations for the critical velocity of ventilation in tunnels have already been extensively studied, for given fire and tunnel (cross-sectional) dimensions. This work describes the potential of a novel concept of an automatic ventilation system based on a PID (Proportional-Integral-Derivative) controller for preventing smoke back-layering under a priori unknown fire size. A few different tunnel dimensions and heat release rates (HRR) are addressed in order to illustrate how the ventilation velocity adapts automatically. Two methods to identify the location of the smoke front are proposed, based on temperature information. Simulation results show that the proposed approach can control the smoke well, preventing back-layering in real-time. It has excellent adaptability for the unknown fire size, which is considered an advantage compared to a system based on constant ventilation velocity. The smoke front becomes stable more quickly for higher HRR, while it takes longer to stabilize for increased tunnel height. This relates to the flame height: sufficiently high flames (compared to the tunnel height) induce a fast rise in temperature, which is the input parameter for the PID system. The tunnel width therefore has little impact. The values for the critical velocity and for the ventilation velocity at maximum smoke back-layering length (BL), as obtained with the proposed method, agree well with existing correlations. The influence of the set-point value for temperature is briefly discussed.

Control of carbon dioxide concentration in headspace of multiple flasks using both non-electric bellows pump and shaking incubator

Current methods of controlling gas in the headspace involve constant speed aeration and proportional-integral-differential (PID) controlled aeration using improved monitoring devices or gas cylinders. However, these approaches are restricted and inconvenient to use. In this study, we propose a method to control the CO2 concentration in the headspace while maintaining the convenience of shake-flask culture. A combination of a non-electric bellows pump for shake-flask (NeBP-sf) and a CO2 incubator was used to control the flask gas phase by shaking without additional external power. The CO2 half-life, as an indicator of the ventilation ability of the system, was measured using a circulation direct monitoring and sampling system, and the NeBP-sf was optimised. The ventilation capacity varied depending on the shaking speed, and under optimal conditions, was 10 min compared with 45 min when only a breathable culture plug was used. In conventional microbial shaking culture, the CO2 concentration in the flask gas phase remained higher than the 5% set-value with a maximum of 9%, resulting in a large concentration difference with the set point. Therefore, the ventilation capacity of the conventional shake-flask culture was insufficient for aerobic culture. Cultivation of Escherichia coli and Lactiplantibacillus plantarum using the system showed no significant difference between the set point and real point values. Thus, the system combined an NeBP-sf and a gas incubator built-in shaking table to achieve the reproducibility of gas control while maintaining a high level of convenience.

Automated Barometric Chamber for Entomology Experiments: Arthropods’ Behavior and Insect-Plant Interactions

Insect behaviors, such as flying, oviposition, parasitism, mating/calling, response to semiochemicals, and others, might be influenced by barometric pressure fluctuations. Abiotic factors controlled in the laboratory facilitate the observation of particularities related to development, behavior, and/or habits of arthropods and plants and their interactions. This study aimed to design an automated barometric chamber for research on arthropod behaviors and insect–plant interactions in the laboratory. The barometric chamber is a transparent box equipped with a single-board computer. An air pump and two proportional solenoid valves were used as actuators to control the air flow, while barometric pressure, air humidity, and temperature sensors were used to monitor the conditions within the chamber. A graphical user interface to operate the barometric chamber was developed to run in a web browser. The barometric chamber was designed to allow the barometric pressure to be changed by up to 15 hPa with respect to the local barometric pressure. In addition, the control system makes it possible to set the rise/fall time (ramp) corresponding to the duration in which a change of pressure will be conditioned. Short- and long-term evaluations demonstrated that the control system can assure pressure stability of ±0.1 hPa with respect to the setpoint value. For demonstration purposes, two experiments were carried out to evaluate the influence of barometric pressure on the feeding activity of Euschistus heros and Diabrotica speciosa. For E. heros, the number of stylet sheath was significantly increased under high pressure conditions compared to the low pressure. However, for D. speciosa, there was no statistical difference in leaf consumption at the evaluated testing conditions.

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO2) has received increasing attention as an alternative source of carbon-based fuels. As CO2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Lastly, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO2.

Design and Simulation of Utilization of Solar Cells as Battery Chargers CC-CV (Constant Current-Constant Voltage) Method with Fuzzy Control

In a country with a tropical climate, the use of the sunlight is very important. Thus, to be able to apply, a solar power conversion system is needed into a source of electrical energy. The use of electrical equipment that is quite high will increase the consumption of electrical power so that people spend more and more on electricity costs. A battery is a device consisting of electrochemical cells that can store electrical energy. Overcharging the battery causes the battery to be susceptible to damage. So that the process of charging the battery becomes important, to get maximum attention and good efficiency. In this study, the use of solar cells with battery chargers using the CC-CV (Constant Current-Constant Voltage) Fuzzy Control method uses a solar cell to convert sunlight into electrical energy. The specifications of the solar cell used are 100 WP, while the charging process uses a DC-DC Sepic Converter. DC-DC Sepic Converter can increase efficiency and output polarity that is not reversed. This system is used to charge the lead-acid battery of 12 Volt 20 Ah. The charging method used is constant current-constant voltage (CC-CV) using Fuzzy Logic Control to adjust the duty cycle so that the converter output is by the constant current - constant voltage (CC-CV) planning. The constant current - constant voltage (CC-CV) method was chosen because it can provide good efficiency in charging time and the addition of the Constant Voltage method after Constant Current is enabled to keep the voltage at the setpoint and avoid overvoltage during the charging process. Sepic Converter is used to maintain the value of the voltage set point at 14.4 Volts and 6 Ampere for battery charging current.

Kontrol Suhu Air Pada Boiler Dengan Energi Gas Metana

Currently, there are many industries that use alternative energy, namely waste energy that is converted into electrical energy, one of which is in the garden sector. Cow dung waste is processed into electrical energy at the Biogas Power Plant, some power plants are usually located in high mountains, which have a low ambient temperature, in this case 21°C. For the purpose of increasing methane gas production, efforts to increase the temperature of the cigar liquid above the ambient temperature of 21°C are attempted by adding boiler water with energy from the rest of the flue gas Covered In-Ground Anaerobi Reaktor (CIGAR) itself a liquid above 21°C. In this case, the waste waste Covered In-Ground Anaerobi Reaktor (CIGAR) is covered with a small Covered In-Ground Anaerobi Reaktor (CIGAR), to accommodate the methane waste. The methane will be used as energy to heat the air which has an initial average (21°C). The result of the air match will go to the mixing tank. The setpoint value at the stove temperature is 150-200 °C and the setpoint value at the maximum air temperature is 55 °C. In the air heating process, when the setpoint value is reached, the microcontroller will instruct the relay to turn off the gas by closing the gas input valve. The gas pressure needed for heating in the water heating process is about 2.2 KPa with a small gas discharge depending on conditions Covered In-Ground Anaerobi Reaktor (CIGAR) in the field. As a result, the water obtained has an average air temperature of 24.2°C with an average air discharge of 2 m⁄s × 379 .

Study on the closed-loop combustion control for a diesel engine by using a dynamic-target online prediction model

In-cylinder pressure-based closed-loop combustion control is a critical technology used to address the issue of cylinder-to-cylinder inhomogeneity throughout the life cycle of a modern diesel engine. Nevertheless, closed-loop combustion control technology (CLCC), which requires a reference or set-point that the control attempts to achieve, causes an increase in the operating maps and the complexity of the calibration effort. In this study, a reference governor-based combustion control (RGCC) is developed in order to reduce the cylinder-to-cylinder combustion dispersions and the calibration effort associated with enabling of the CLCC simultaneously. The RGCC consists of a dynamic-target online prediction model and an indicated mean effective pressure (IMEP) control strategy. The dynamic-target prediction model is designed based on the IMEP of the engine real-time tests; it uses a modified Kalman filter algorithm, which provides the set-point value for the IMEP control strategy. The effectiveness of the proposed RGCC is experimentally validated on a commercial diesel engine test bench under steady-state and transient-state conditions. The experimental results show that the RGCC displayed far better transient tracking performance than the closed-loop IMEP control based on MAP. Furthermore, the controller can effectively reduce cylinder-to-cylinder IMEP variations under steady-state and transient-state conditions.

Error driven Fuzzy Logic Controller (FLC) for Spherical Mobile Robot: Simulation & Experimental Performance Analysis

Spherical mobile robot is a ball-shaped mobile robot that capable to move from one place to another to perform the desired tasks. The challenge for spherical mobile robot falls within control algorithm aspects, where it is nonlinear and highly under-actuated increases the difficulties to control its motion. In this paper, the potentiality of intelligent controller is explored to control the motion spherical mobile robot. An error driven Fuzzy Logic controller (FLC) which consist of modular of two Proportional-Derivative (PD) type FLC and a Proportional-Integral (PI) type FLC were designed and tested to control the position of the spherical mobile robot, angle of the rotation of the pendulum and the angular velocity of the spherical mobile robot, respectively. The performance of the controller was then optimized by tuning the input and output gain of the controller with Particle Swarm optimization (PSO) method. The performance of the robot without the controller and with optimized controller were compared and analysed. As a result, the performance of the system in the form of rise time and settling time of the robot to control its position managed to be reduced by 86% and 89% respectively with optimized PD-type. For PD-type FLC to control the rotation of the pendulum, it was capable to eliminate the overshoot and reduce it settling time by 35.5%. While the optimized PI-type FLC has managed to eliminate the steady state error and achieved it desired velocity setpoint in 1.2s. Lastly, all the designed controllers were successfully tested on the prototype of the spherical mobile robot to study the performance of the spherical mobile robot. However, the overall performance of the implemented designed controller has shown a minimum effectiveness. The controller managed to achieve the setpoint value but with low stability which result from the less accuracy of the hardware sensors.

A neural-network based variable universe fuzzy control method for power and axial power distribution control of large pressurized water reactors

This paper proposes a neural network-based variable universe fuzzy controller (NN-VUFC) for power and axial power distribution control of large pressurized water reactors (PWRs). First, a two-node reactor kinetics model is introduced, and the fuzzy inference rules are formulated for the reactor power and axial power distribution control according to their deviations from corresponding setpoint values. Then, based on the idea of variable universe, the scaling factor of the input fuzzy universe is designed to maximize the utilization of fuzzy rules. Finally, a neural network is designed to optimize the proportionality coefficient of the scaling factor online to further improve the fuzzy control performance. Simulation results of the CPR1000 reactor under typical transient conditions show that the NN-VUFC can realize good control of both the reactor power and axial power distribution, and its control performance is effectively improved compared to traditional fuzzy controllers.

Speed Sensorless Fault Tolerant Control for DC Servo Motor with Current Sensor Fault

This paper discusses the application of a fault tolerant control scheme in sensorless speed control for dc motors. The sensorless speed system works by estimating the speed using the current measurement value. The novelty of the proposed scheme is a speed estimator capable of accommodating current sensor faults, which is the only measurement information. The technique used is a modification of the Extended State Observer (ESO) where the residual is not only used to estimate the state, but also to estimate the load torque. In this case, the load torque is treated as a changing input parameter and it is estimated using the observer's normal approach. The modified ESO estimation result is in the form of speed and current estimate, and then fed to the state feedback control with the integrator in order to maintain the speed at the setpoint value. In order to test the proposed control system, numerical simulations are carried out and comparisons are made between the control system with ESO and with modified ESO. From the results of this simulation experiment, the conclusion is that the modified ESO control system is proven to be able to overcome changes in load torque and setpoint as well as sensor faults that occur.

Differential Impacts on Tensional Homeostasis of Gastric Cancer Cells Due to Distinct Domain Variants of E-Cadherin.

Simple SummaryTensional homeostasis describes the ability of cells and tissues to maintain their internal mechanical tension stable at a set point value. A breakdown of tensional homeostasis is the hallmark of disease progression, including cancers. In cancers of epithelial origin, this phenomenon is closely associated with dysfunction of E-cadherin. In this study, we investigated how E-cadherin mutations identified in patients with cancer affect tensional homeostasis. Our results show that mutations affecting the juxtamembrane and intracellular domains of E-cadherin are detrimental for tensional homeostasis of gastric cancer cells.In epithelia, breakdown of tensional homeostasis is closely associated with E-cadherin dysfunction and disruption of tissue function and integrity. In this study, we investigated the effect of E-cadherin mutations affecting distinct protein domains on tensional homeostasis of gastric cancer cells. We used micropattern traction microscopy to measure temporal fluctuations of cellular traction forces in AGS cells transfected with the wild-type E-cadherin or with variants affecting the extracellular, the juxtamembrane, and the intracellular domains of the protein. We focused on the dynamic aspect of tensional homeostasis, namely the ability of cells to maintain a consistent level of tension, with low temporal variability around a set point. Cells were cultured on hydrogels micropatterned with different extracellular matrix (ECM) proteins to test whether the ECM adhesion impacts cell behavior. A combination of Fibronectin and Vitronectin was used as a substrate that promotes the adhesive ability of E-cadherin dysfunctional cells, whereas Collagen VI was used to test an unfavorable ECM condition. Our results showed that mutations affecting distinct E-cadherin domains influenced differently cell tensional homeostasis, and pinpointed the juxtamembrane and intracellular regions of E-cadherin as the key players in this process. Furthermore, Fibronectin and Vitronectin might modulate cancer cell behavior towards tensional homeostasis.