scholarly journals A model predictive controller of the blowing mode during basic oxygen furnance process

2021 ◽  
pp. 61-66
Author(s):  
Oleksandr Stepanets ◽  
Yurii Mariiash
Keyword(s):  
Mathematical Model ◽  
Cast Iron ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Basic Oxygen Furnace ◽  
Scrap Metal ◽  
Basic Oxygen ◽  
The World ◽  
The Cost

Today in Ukraine and the world, the problem of energy saving and reducing the cost of smelted steel is state of art. Metallurgical enterprises are developing in conditions of fierce competition, the main reason is that Ukrainian products are extremely energy-intensive due to the depreciation of fixed assets and outdated technological processes. The basic oxygen furnace process is a process of producing steel from liquid cast iron with the addition of steel scrap to the converter and blowing oxygen from above through a water-cooling lance. Nowadays, the production of steel by BOF process is the most popular in the world and is becoming increasingly common. The main disadvantage of the basic oxygen furnace is the need to provide the initial amount of heat (in the form of liquid cast iron) and as a consequence - restrictions on the processing of scrap metal. Reducing the cost of basic oxygen furnace steel is achieved by increasing the share of scrap metal by increasing the degree of afterburning of CO to CO2 in the cavity of the converter, by optimal control of the parameters of the blast mode using model-predictive control. The principle of model-predictive control is based on a mathematical model of the plant. This approach minimizes the functional that characterizes the quality of the process. The linear-quadratic functional was chosen. A forecasting model is proposed taking into account the constraint on changing the position of the lance and the pneumatic oxygen supply valve. It was found that the change in the rate of decarburization of the metal depends on the distance of the lance to the level of the quiet bath and affects the degree of afterburning of CO to CO2. The decarburization process is non-stationary, described by a first-order inertial model, the transfer coefficient and time constant of which depends on the melting period and the duration of the purge. The mathematical model of the blast mode of oxygen-converter melting has been improved, taking into account the influence of the blast intensity on the decarburization process of the bath, which allowed to increase the accuracy and quality of blast control in terms of changing oxygen flow during purging. The simulation results of the automatic control system show that the model-predictive regulator provides the required level of carbon dioxide in the converter gases when the flow rate of oxygen for purge changes.

Optimal control of the blowing mode parameters during basic oxygen furnace steelmaking process

2021 ◽  
pp. 94-96
Author(s):  
Yurii Mariiash ◽  
Oleksandr Stepanets
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
System Modeling ◽  
Basic Oxygen Furnace ◽  
Steel Scrap ◽  
Quadratic Functional ◽  
Steelmaking Process ◽  
Basic Oxygen ◽  
Control Approach

The oxygen converter is intended for production of steel from liquid cast iron and steel scrap at blowing by oxygen. Nowadays, Basic Oxygen Furnace process is the main method for steelmaking. The main disadvantage of the basic oxygen furnace is the limited ability to increase the part of scrap metal. The task of the proposed approach is to control of the blowing mode parameters to establish the optimal level of CO2 that will ensure a minimum specific cost of steel in the presence of restrictions and boundary conditions of basic oxygen furnace steelmaking process. A model predictive control taking into account the constraints on the input signals and the quadratic functional is proposed.  The design of Model Predictive Control is based on mathematical model of an object. This approach minimizes the cost function that characterizes the quality of the process. The result of the automatic control system modeling shows that the Model Predictive Control approach provides retention of carbon dioxide level when oxygen consumption is changing. The obtained quadratic functional is optimized to find the optimal control of blowing parameters.

scholarly journals MODEL PREDICTIVE CONTROL APPLICATION IN THE ENERGY SAVING TECHNOLOGY OF BASIC OXYGEN FURNACE

2020 ◽  
Vol 10 (2) ◽  
pp. 70-74
Author(s):  
Oleksandr Stepanets ◽  
Yurii Mariiash
Keyword(s):  
Oxygen Consumption ◽  
Chemical Composition ◽  
Model Predictive Control ◽  
Energy Saving ◽  
Predictive Control ◽  
Basic Oxygen Furnace ◽  
Basic Oxygen ◽  
Melting Period ◽  
Energy Saving Technology ◽  
Control Application

The fulfilment of the condition for the simultaneous achievement of the desired chemical composition and temperature of the metal is ensured by controlling the oxygen consumption and the position of the oxygen impeller lance. The method for solving Model Predictive Control with quadratic functionality in the presence of constraints is given.  Implementation of the described solutions will contribute to increasing the proportion of scrap and reducing the melting period without changing of technological process.

Network analysis models – the secret for success

2004 ◽  
Vol 4 (5-6) ◽  
pp. 383-388
Author(s):  
D.M. Rogers
Keyword(s):  
Mathematical Model ◽  
Field Data ◽  
Essential Element ◽  
Distribution Networks ◽  
Automatic Calibration ◽  
Supply And Distribution ◽  
The World ◽  
Optimum Approach ◽  
Analysis Models

Water is a fundamental necessity of life. Yet water supply and distribution networks the world over are old and lacking in adequate maintenance. Consequently they often leak as much water as they deliver and provide an unacceptable quality of service to the customer. In certain parts of the world, water is available only for a few hours of the day. The solution is to build a mathematical model to simulate the operation of the real network in all of its key elements and apply it to optimise its operation. To be of value, the results of the model must be compared with field data. This process is known as calibration and is an essential element in the construction of an accurate model. This paper outlines the optimum approach to building and calibrating a mathematical model and how it can be applied to automatic calibration systems.

scholarly journals Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3467 ◽  
Author(s):  
Po Li ◽  
Ruiyu Li ◽  
Haifeng Feng
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Single Phase ◽  
Tracking Error ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Finite Control ◽  
Control Set ◽  
The Cost

Inverters are commonly controlled to generate AC current and Total Harmonic Distortion (THD) is the core index in judging the control effect. In this paper, a THD oriented Finite Control Set Model Predictive Control (FCS MPC) scheme is proposed for the single-phase inverter, where a optimization problem is solved to obtain the switching law for realization. Different from the traditional cost function, which focuses on the instantaneous deviation of amplitude between predictive current and its reference, we redesign a cost function that is the linear combination of the current fundamental tracking error, instantaneous THD value and DC component in one fundamental cycle (for 50 Hz, it is 0.02 s). Iterative method is developed for rapid calculation of this cost function. By choosing a switching state from a FCS to minimize the cost function, a FCS MPC is finally constructed. Simulation results in Matlab/Simulink and experimental results on rapid control prototype platform show the effect of this method. Analyses illustrate that, by choosing suitable weight of the cost function, the performance of this THD oriented FCS MPC method is better than the traditional one.

scholarly journals Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4158 ◽  
Author(s):  
Hancheol Cho ◽  
Giorgio Bacelli ◽  
Ryan G. Coe
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Feedback Form ◽  
Linear Controller ◽  
Prediction Horizon ◽  
The Cost ◽  
Input And State Constraints

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.

Mathematical Representation of Quality of Service (QoS) Parameters for Internet of Things (IoT)

2020 ◽  
pp. 364-376
Author(s):  
Sandesh Mahamure ◽  
Poonam N. Railkar ◽  
Parikshit N. Mahalle
Keyword(s):  
Mathematical Model ◽  
Quality Of Service ◽  
Internet Of Things ◽  
Gap Analysis ◽  
Literature Survey ◽  
Mathematical Representation ◽  
The World ◽  
Qos Parameters ◽  
The Mathematical Model

Now we are in the era of ubiquitous computing. Internet of things (IoT) is getting matured in various parts of the world. In coming few years' billions and trillions of things will be connected to the internet. To deal with these huge number of devices in a network we need to consider Quality of Service (QoS)parameters so that system operations can be performed in a smoother way. Mathematical modelling of these QoS parameters gives an idea about which factors are needs to consider while designing any IoT-enabled system at the same time it will give the performance analysis of the system before implementation. In this paper comprehensive literature survey is done to discuss various issues related to QoS and gap analysis is also done for IoT Enabled systems. This paper proposes general steps to build a mathematical model for a system. It also proposes the mathematical model for QoS parameters like reliability, communication complexities, latency and aggregation of data for IoT. To support proposed mathematical model proof of concept also given.

scholarly journals A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Weide Guan ◽  
Shoudao Huang ◽  
Derong Luo ◽  
Fei Rong
Keyword(s):  
Computational Complexity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Performance ◽  
Multilevel Converter ◽  
Multilevel Converters ◽  
Computational Burden ◽  
Modular Multilevel Converters ◽  
The Cost ◽  
Fast Dynamic

In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.

scholarly journals A Simplified Model Predictive Control for T-Type Inverter with Output LC Filter

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 31 ◽  
Author(s):  
Van-Quang-Binh Ngo ◽  
Minh-Khai Nguyen ◽  
Tan-Tai Tran ◽  
Young-Cheol Lim ◽  
Joon-Ho Choi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Performance Simulation ◽  
Control Scheme ◽  
Finite Control ◽  
Lc Filter ◽  
The Cost ◽  
And Control ◽  
Measurement And Control ◽  
Selection Of

In this paper, a model predictive control scheme for the T-type inverter with an output LC filter is presented. A simplified dynamics model is proposed to reduce the number of the measurement and control variables, resulting in a decrease in the cost and complexity of the system. Furthermore, the main contribution of the paper is the approach to evaluate the cost function. By employing the selection of sector information distribution in the reference inverter voltage and capacitor voltage balancing, the execution time of the proposed algorithm is significantly reduced by 36% compared with conventional model predictive control without too much impact on control performance. Simulation and experimental results are studied and compared with conventional finite control set model predictive control to validate the effectiveness of the proposed method.

Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

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