Simulation Model Of Plant Research Articles

The Possibilities of a Multifunctional Control and Measure-Ment System Installed in a Small Hydropower Plant

Abstract In 2020, a fully automated hydropower plant was launched on the Guber River near the town of Kotkowo. The plant is operated by a master control and measurement system, which collects data to evaluate the operation of selected systems of the facility. The number and location of sensors controlling the parameters of hydroelectric systems are selected accordingly, to collect complete information from all sensors and analyze the operation of hydroelectric systems in real time. In addition, storing all the controlled parameters allows analyzing the plant’s operation over longer periods. This work presents the possibilities of this measurement system, as well as the measurement results obtained in the tested object. Analyzing the operation of the control and measurement system as well as the collected and archived data will be the foundation for a simulation model of a hydropower plant. The model will be helpful in optimizing the operation of existing hydroelectric plants in terms of energy production per unit volume of water, and in designing new ones on existing barrage.

Increasing the Efficiency of a Diesel-Generator Power Plant

A variant of a diesel generator power plant consisting of a conventional diesel-generator set and a variable-speed diesel-generator set is considered. A transformer-free topology of the power circuit of the variable-speed diesel-generator set is proposed on the basis of an active voltage rectifier, a mains inverter, and a buffer energy storage device. The block diagram for controlling a diesel-generator power plant is developed. The autonomous and parallel operation modes of the diesel-generator sets are considered. A block diagram of the speed controller on the basis of intelligent controls is proposed for the variable-speed diesel generator set. The simulation model of the diesel-generator power plant is implemented in the MATLAB/Simulink suite. Results of simulation modeling of the power plant’s dynamic operational modes under load switching are presented. The variable-speed diesel-generator set is shown to provide energy-efficient operation of the power plant and compliance with the requirements for the quality of generated electricity when the internal combustion engine runs at low energy efficient rotation speeds in the shared load mode.

Requirements for Validation of Dynamic Wind Turbine Models: An International Grid Code Review

Wind power is positioned as one of the fastest-growing energy sources today, while also being a mature technology with a strong capacity for creating employment and guaranteeing environmental sustainability. However, the stochastic nature of wind may affect the integration of power plants into power systems and the availability of generation capacity. In this sense, as in the case of conventional power plants, wind power installations should be able to help maintain power system stability and reliability. To help achieve this objective, a significant number of countries have developed so-called grid interconnection agreements. These are designed to define the technical and behavioral requirements that wind power installations, as well as other power plants, must comply with when seeking connection to the national network. These documents also detail the tasks that should be conducted to certify such installations, so these can be commercially exploited. These certification processes allow countries to assess wind turbine and wind power plant simulation models. These models can then be used to estimate and simulate wind power performance under a variety of scenarios. Within this framework, and with a particular focus on the new Spanish grid code, the present paper addresses the validation process of dynamic wind turbine models followed in three countries—Spain, Germany and South Africa. In these three countries, and as a novel option, it has been proposed that these models form part of the commissioning and certification processes of wind power plants.

Coupled Gas-Exchange Model for C4 Leaves Comparing Stomatal Conductance Models.

Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for leaves incorporating two widely used stomatal conductance submodels: Ball–Berry and Medlyn models. The output variables of the model includes steady-state values of assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal concentrations, and other leaf gas-exchange attributes in response to light, temperature, , humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball–Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.

Электростанция на базе дизель-генераторной установки переменной частоты вращения и буферного накопителя энергии

The article describes the diesel generator complex which consists of two diesel generators. In order to save fuel, it is advisable to choose one diesel generator of inverter type, i.e. to switch it to a variable speed mode. The buffer storage device is used as a part of the power plant in order to ensure the required parameters of the generated electricity under peak loads, as well as when the generator is operating at the limit of rated power. There has been given the control algorithm and transient processes of the charge / discharge of the buffer storage device in a diesel generator power plant. The power plant that consists of a classic diesel generator and a variable speed diesel generator unit (valved generator) has been chosen as an object research. A block diagram of the power plant conducting is proposed, as well as a power transformer topology of a variable-speed diesel generator based on a step-down pulse-width converter. There has been presented an algorithm for controlling the connection of a buffer storage device depending on its charge and the load power value both in stand-alone mode and in parallel operation of generator sets. In the MatLab Simulink computer environment a simulation model of a power plant was developed and transient processes of load switching, as well as charge / discharge of a buffer energy storage device were studied. An electrical circuit diagram of an experimental bench of a variable-speed diesel generator set is illustrated. The results of experimental studies of dynamic modes of operation on a prototype variable speed diesel generator with a power of 3.2 kW are also presented.

Energy and economic analysis of a small hybrid solar-geothermal trigeneration system: A dynamic approach

This work presents a dynamic simulation model of a prototypal renewable plant producing electricity, heat and cool. The hybrid system consists of a 6 kWe Organic Rankine Cycle (ORC), a 17.1 kWf absorption chiller, a geothermal well, a 25 m2 evacuated solar collector field, a 200 kWt biomass heater and a 45.56 kWh electric energy lithium-ion storage system. The geothermal well at 96 °C, in combination with the solar field, supplies the thermal energy driving the ORC. This simulation model is implemented in TRNSYS environment. In order to evaluate the thermoeconomic performance of this renewable trigeneration plant, a suitable case study is analysed: a bar and soccer centre in the area of Campi Flegrei (Naples). By the simulation, interesting results in terms of energy and environmental performance are obtained. The proposed renewable power plant achieves a primary energy saving of 94.54% and a reduction of CO2 emissions equal to 97.36%. The high total capital cost of the plant significantly affects the payback period of 16.7 years. However, in the future European energy scenarios, where a full decarbonization will be achieved and the capital cost of the renewables will significantly decrease, the use of these technologies will be pivotal and significantly profitable.

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

Techno-economic barriers of an industrial-scale methanol CCU-plant

Global anthropogenic CO2 emissions are expected to peak to 40 Gt in 2020. If these emissions are not mitigated climate change and global warming will further aggregate. Meanwhile, demand for products and fuels produced from fossil raw materials are increasing. CO2, however, can be considered as feedstock for certain materials and processes. If CO2 is catalytically synthesised with H2 it can form a variety of hydrocarbons, such as methane, methanol (MeOH), higher alcohols, and liquid fuels. In this paper, a simulation model of a MeOH plant using CO2 and H2 as feedstock was developed in Aspen Plus™. This is the first plant studied at an industrial-scale comparable with fossil MeOH plant units. The plant produces 5 kt chemical-grade MeOH daily that can be used as raw material for the chemical industry or as a fuel. The kinetic model, considering both CO and CO2 as the source of carbon, accomplished high overall CO2 conversion rate and close to stoichiometric raw material utilisation. Under the current market conditions, the MeOH plant is not feasible even at this scale. The most significant cost parameter making the plant non-viable is attributed to the high cost of H2 produced by water electrolysis. A series of sensitivity analyses revealed that co-selling of O2 by-product from the electrolyser and lowering the H2 cost price have a significant factor in achieving a more competitive levelised cost of MeOH. These economic results are analysed in-depth with previous studies to reveal the effect of different economic assumptions.

An integrated methodology for the economic and environmental assessment of a biorefinery supply chain

A supply chain network MILP model, developed by means of AIMMS software, and a process plant simulation model, developed by means of Aspen Plus®, are combined for the optimization of a biorefinery network. Optimization of the supply chain network is initially addressed using literature process and economic data. The results are used as input in the Aspen Plus® model where the technical and economic performance of the biorefineries is calculated rigorously. The two computational tools are iteratively executed until convergence on number, locations and size of the biorefineries and on process yield to products and total costs is achieved. The final results are used to perform the Economic Value and Environmental Impact (EVEI) analysis of the overall biorefinery network. The methodology is applied to a case study concerning the deployment of cereal straw in Germany to produce ethanol, ethyl levulinate and electricity. Optimization results reveal that the wheat straw supply network with four biorefineries is economically feasible and determines an environmental margin in terms of equivalent emissions savings of about 4 Mt of CO2 per year.

Combined Electric Power Plant Simulation Model

The article is dedicated to a simulation model of a wind/solar power plant. A variable speed diesel generator set is used as a backup energy source. A matrix frequency converter is used to control frequency of the gene­rated voltage. The simulation model is implemented in the MATLAB Simulink environment. Oscillograms of currents and voltages are obtained when the model operates with an active inductive electrical load with the variable load value in the 1000–4000 VA range. During operation with a rated power load, the deviation of the line voltage parameters lies within the normal range (load voltage drop does not exceed 4% of the nominal value, total harmonic distortion does not exceed 2% in the 0–2000 Hz range). The constructed model can be used in the design of energy facilities, as well as in the development and testing of algorithms for control and monitoring systems.

Research on Source-Network Coordination Voltage Control Strategy of Photovoltaic Power Plant Considering the Stability of Inverter Port Voltage

At present, the reactive power distribution method considering the reactive power adjustment capacity of the inverter in the photovoltaic (PV) power plant will lead to the output voltage of the inverter exceeding the limit. In particular, the PV inverter adopts the constant reactive power control to support the voltage at point of common coupling (PCC), which has a good effect in the steady-state operation. However, when the system is subject to transient disturbance, the PV inverter cannot flexibly support the voltage level of the system by changing the reactive power output. Therefore, this paper proposes a source-network coordination control strategy aiming at the stability of bus voltage at PCC and inverter port voltage. Firstly, the paper introduced the advantages of constant voltage control of PV inverter. Then, the voltage and reactive power control objectives of the PV Inverter in the PV power plant were calculated by considering the PCC voltage, tie lines, power collection lines, the power of transformers and the voltage loss to realize the voltage and reactive power balance control between the inverters. Finally, an actual PV power plant simulation model was built to analyse the PCC voltage under the two conditions of PV output fluctuation and system fault to verify the effectiveness of the control strategy proposed in this paper. The proposed strategy has engineering application value for the realization of closed-loop voltage control between the power grid and PV power plant and the improvement of the active response ability to the PCC voltage.

Simulation Model of LPG Recovery Plant to Optimize the Process Parameters

Simulation Model of LPG Recovery Plant to Optimize the Process Parameters

Electromagnetic compatibility of receivers in the power supply system with induction heating units

The problem of electromagnetic compatibility of users that have a mutual supply center is examined. On the basis of field studies, the levels of higher harmonic components of current and voltage were determined at an operating steel plant with induction heating electrical installations. Simulation model of steel plant with electrical installations of induction heating is presented, which includes controlled filter-compensating devices and fuzzy logic controller, for which product rules of fuzzy interference are formulated. It is shown that the use of a fuzzy logic controller allows limiting the level of higher harmonics of current and voltage during operation of the induction furnace.

Thermal balance of large scale parabolic trough plants: A case study

The main control objective in commercial solar parabolic plants is to track the average temperature of all the loops around a reference set by the operator, by manipulating the flow of a synthetic oil. Due to the large number of loops existing in current solar plants and the vast extension that they cover, obtaining a precise knowledge of every loops efficiency becomes a very challenging task. Parameters such as loops reflectivity, tube absorptance, optical efficiency may exhibit a great disparity due to dirt accumulated and atmospheric conditions. Because of this, the most efficient loops achieve higher temperatures and the collectors may have to be set out of focus in many cases.This paper presents a case study on a simulation model of a large scale parabolic plant, 50 MW, in which a control of the aperture of the solar field loops inlet valves is applied through a nonlinear optimization problem. The goal is to reduce the imbalance of loops temperature caused by the difference in efficiencies. The idea is opening the input valves of the most efficient loops to increase the incoming flow-rate and reducing the flow in the less efficient loops. This will prevent, in many cases, the activation of the defocus control avoiding energy losses and minimizing the deterioration of actuators.The optimization problem will need estimations of the loops states and parameters. An Unscented Kalman Filter is used to estimate the loops temperatures states and a concentrated parameter model is used to estimate the loops efficiencies. A loop clustering is implemented to avoid high computation times. Simulation results showing thermal balance of the field minimizing the defocusing actions are presented.

The Fastest Response Algorithm for Thermal Plants with a Time Delay: Application in a Hybrid System Containing a PID Controller and an Automatic Tuning Unit

The article discusses matters concerned with improving the performance of automatic closed-loop control systems of thermal plants with a time delay in responding to a change in the setpoint. The improvement is achieved by applying the fastest response algorithm (FRA) according to the Pontryagin maximum principle and using linear prediction of the controlled variable. It is shown that, during operation with switching the maximum control outputs applied to a plant with a time delay, linear prediction is inefficient, and self-oscillations may occur in the system. The technical solution proposed for eliminating self-oscillations implies the use of a hybrid closed-loop control system comprising an FRA, a PID controller, and an automatic controller tuning (ACT) unit, which performs the function of determining the plant model parameters and optimizing the controller parameters. The actuator is considered as a proportional section that is used as part of the plant. The control limitations are related to the level of the control output applied to the plant. The ACT unit comprises accelerated controller tuning algorithms that use active plant identification methods based on analyzing the response to an impulse input and two cycles of the excited self-oscillations. These algorithms make it possible to determine four parameters of the second-order plant model with a time delay. The self-oscillations occurring in systems with the FRA and plants with a time delay are eliminated at the end of the transient by making a switchover to PID control. Four embodiment versions of a system with the FRA are analyzed, specifically, those with and without control output reversal and also with using the plant simulation model without a time delay that is obtained from the ACT operating in parallel with the plant. For practical embodiment of the MSRA as part of a hybrid system, it is recommended to use its version without control output reversal. Relations for calculating the controlled variable prediction coefficient in terms of the plant model parameters in a wide range are obtained. Two examples of using the hybrid system equipped with industry-grade controllers for a temperature control system are given: one with the electric heater power controlled using a pulse-width modulator and the other with a constant speed actuator.

Utilization of Food and Agricultural Residues for a Flexible Biogas Production: Process Stability and Effects on Needed Biogas Storage Capacities

Biogas plants can contribute to future energy systems’ stability through flexible power generation. To provide power flexibly, a demand-oriented biogas supply is necessary, which may be ensured by applying flexible feeding strategies. In this study, the impacts of applying three different feeding strategies (1x, 3x and 9x feeding per day) on the biogas and methane production and process stability parameters were determined for a biogas plant with a focus on waste treatment. Two feedstocks that differed in (1) high fat and (2) higher carbohydrate content were investigated during semi-continuous fermentation tests. Measurements of the short chain fatty acids concentration, pH value, TVA/TIC ratio and total ammonium and ammonia content along with a molecular biology analysis were conducted to assess the effects on process stability. The results show that flexible biogas production can be obtained without negative impacts on the process performance and that production peaks in biogas and methane can be significantly shifted to another time by changing feeding intervals. Implementing the fermentation tests’ results into a biogas plant simulation model and an assessment of power generation scenarios focusing on peak-time power generation revealed a considerable reduction potential for the needed biogas storage capacity of up to 73.7%.

Modeling and load follow simulation of CPR1000 Nuclear Power Plant implementing Mechanical Shim control strategy

Load follow operation without changing soluble boron concentration is desirable for reasons of economic efficiency, automatic control capability and effluent reduction. Many researchers have investigated the implementations of Mechanical Shim (MSHIM) strategy to different types of Pressurized Water Reactors (PWRs) to achieve this goal. However, their studies mainly focused on reactor core calculations. This paper presents the modeling and load follow simulation of the CPR1000 Nuclear Power Plant (NPP) implementing MSHIM operation and control strategy in MATLAB/Simulink environment. The whole plant model consists of the Nuclear Steam Supply System (NSSS) and the Balance of Plant (BOP) system. The NSSS model consisting of the one-dimensional nodal reactor core model, nonequilibrium three-region pressurizer model and nonlinear, time-dependent lumped parameters U-Tube Steam Generator (UTSG) model was first developed. The BOP system model including dynamic models of key components, piping lines and valves was also developed. These models were then integrated in MATLAB/Simulink according to their coupled relationships with relevant control systems built to obtain the whole plant simulation model. Based on this model, the typical 12-3-6-3/100-50-100 daily load follow maneuvers with constant soluble boron concentrations at the Beginning of Cycle (BOC) and End of Cycle (EOC) were simulated under the MSHIM control strategy. The simulation results have demonstrated the feasibility and safety of the CPR1000 NPP implementing MSHIM control strategy to achieve daily load follow operations at both BOC and EOC. This study could offer a reference to the engineering application of MSHIM control strategy as well as whole plant simulations of PWRs.

Experimental modelling and plant simulation of spiral concentrators: Comparing response surface methodology and extended Holland-Batt models

Spiral concentrator and spiral plant modelling of chromite bearing ore is investigated in this paper. Spiral product stream responses (grade and recovery) were modelled according to variations in feed conditions (grade, density, flow rate) and primary splitter settings. The two modelling approaches constituted a quadratic response surface and an extension (to accommodate feed conditions) on the original Holland-Batt (1990) spline model. Additional validation of the extended Holland-Batt model behaviour was performed via regression on a second data set, based on hematite separation using spiral concentrators (Sadeghi, 2015). The quadratic model showed higher performance values on the training set; however, the extended Holland-Batt model showed better performance on the test data. Finally, both models were compared in terms of spiral concentrator plant modelling ability (based on an industrial spiral concentration plant). Quadratic response surface models were unable to produce acceptable grade-recovery curve simulations. The extended Holland-Batt model proved to be the superior spiral plant simulation model due to its ability to produce valid grade-recovery curves. The extended Holland-Batt spline-based model shows promise as an improvement over RSM for spiral modelling (and spiral plant simulation). The extended Holland-Batt model can provide the same screening, characterization and optimization functionality of RSM models for single-spiral experimental modelling, with the added benefit of simple extension to spiral plant simulation.

The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals

AbstractSeventeen Sustainable Development Goals (SDGs) were adopted by 193 Governments at the General Assembly of the United Nations in 2015 for achievement by 2030. These SDGs present a roadmap to a sustainable future and a challenge to the science community. To guide activities and check progress, targets and indicators have been and are still being defined. The soil science community has published documents that describe the primary importance of soil for SDGs addressing hunger, water quality, climate mitigation and biodiversity preservation, and secondary relevance of soil for addressing several other SDGs. Soil scientists only marginally participated in the SDG discussions and are currently only peripherally engaged in discussions on targets or indicators. Agreement on several soil‐related indicators has still not been achieved. Involvement of soil scientists in SDG‐based studies is desirable for both developing solutions and increasing the visibility of the soil profession. Inputs into policy decisions should be improved as SDG committee members are appointed by Governments. Possible contributions of soil science in defining indicators for the SDGs are explored in this paper. We advocate the pragmatic use of soil–water–atmosphere–plant simulation models and available soil surveys and soil databases where “representative” soil profiles for mapping units (genetically defined genoforms) are functionally expressed in terms of several phenoforms reflecting effects of different types of soil use and management that strongly affect functionality.

Novel fault diagnosis scheme utilizing deep learning networks

Information availability and processing advances have allowed automated fault diagnosis methods to gain popularity in nuclear industry. However, stringent safety requirements coupled with nuclear industry requires that such methods avoid any kind of misdiagnosis. In manuscript, a novel fault diagnosis model is being proposed which can not only diagnose faults in an early time frame but also has the ability to identify fault scenarios that cannot be confidently classified. Moreover, for each type of fault identified, severity of the fault can also be calculated. The proposed method utilizes Principle Component Analysis & Deep Learning networks of Long Short Term Memory and Convolution Neural Network. The study was conducted on simulation model of IP-200 nuclear power plant using RELAP5/MOD 4.0 thermal-hydraulic code. Proposed fault diagnosis model was verified using different operational states along-with 4 different fault types and 1 blind case. Simulation data suggests that proposed model is a feasible scheme for fault diagnosis of nuclear power plant.