Belt Conveyor System Research Articles

A Review on Faulty Product Detection and Separation System for Measuring Accurate Weight of SOAP

Abstract: Wireless sensor networks have become a new information collection and monitoring solution for variety of product such as soap. All product manufacturing unit need to have a faulty product detection and separation system in order to maintain good product accuracy by using conveyor belt system. This proposed system detect faulty product according to required weight by using load cell and proximity sensor, thus with the help of electric actuator faulty products are separated according to the weight. This system is powered by a DC Geared motor, and the system consist components Rollers (nylon) and silicon belt are used to develop a conveyor belt system. This paper deals with a review on faulty product detection and separation system Keywords: Conveyor systems, proximity sensor, load cell, Fault detection.

LIVE Digital Twin: Developing a Sensor Network to Monitor the Health of Belt Conveyor System

Industry 4.0 requires developing smart systems to maximize the uptime of machines and components. Digital Twins can be defined as a real time exchange of the information between a physical asset and a virtual portrayal in a bidirectional manner. This relationship is best established with a sensor network. LIVE Digital Twin presents a methodology to design model-based Digital Twins for asset management through sensors. This methodology is increasingly useful when the fault history of an asset is not readily available. The LIVE Digital Twin methodology consists of four principle phases, Learn, Identify, Verify, Extend. The goal of this research is to review the application of the LIVE Digital Twin methodology on a case study of a Belt Conveyor System found in the mining industry. Belt Conveyor Systems and their rollers are critical in material transportation and are susceptible to various faulty cases. Using a multi fidelity approach, a case study demonstrates the first two phases of LIVE Digital Twin and identifying the sensor locations. The study concludes with the successful location of 2 sensors on a subassembly of a Belt Conveyor System frame.

Automated IR Image Segmentation for Identification of Overheated Idlers in Belt Conveyor Systems Based on Outlier-Detection Method

Automated IR Image Segmentation for Identification of Overheated Idlers in Belt Conveyor Systems Based on Outlier-Detection Method

Waiting time distribution in single-channel deterministic flow lines with discrete inter-arrival time distributions

Although previous studies proved that deterministic flow lines can be analysed separately by an exact decomposition method called a channel method and that the delay an item experiences in each machine possesses a Markovian property, the exact performance measures for the system with general arrival process cannot be obtained due to the lack of the results on relevant GI/D/1 queue. The solution obtained only for the system with specific arrival patterns such as geometric distribution or JIT arrivals considering setups. In this paper, exact decomposition methods are investigated through the systems consisting of one channel. Assuming general and discrete inter-arrival times, we introduce a procedure to calculate the equilibrium probabilities for waiting time distributions. It is demonstrated that probabilities can be computed by a matrix analytic method and recursions based on the decomposition methods. Inspired by the steel product manufacturing process, two-stage conveyor belt systems are analysed as an application. [Received: 28 July 2020; Accepted: 1 March 2021]

Power Consumption Model of Permanent-Magnet Direct-Drive Belt Conveyor System Based on GACO–BP

Permanent-magnet direct-drive belt conveyors (PMDDBCs) rotate at high speed most of the time, resulting in a large number of invalid energy consumption. To realize the speed regulation of PMDDBC, it is necessary to clarify the relationship between the belt speed, coal quantity of the conveyor and total power of the system. Based on the BP neural network, this paper establishes the power consumption model of PMDDBC, which is related to coal quantity, belt speed and total power. Furthermore, an improved hybrid algorithm (GACO) that combines the advantages of genetic algorithm (GA) and ant colony optimization (ACO) is proposed to optimize the BP power consumption model. The GACO–BP power consumption model is obtained. The original power consumption model is compared with the GACO–BP power consumption model through experiments. Results demonstrate that the GACO–BP power consumption model reduces various prediction errors, while the optimization ability, prediction accuracy and convergence speed are significantly enhanced. It provides a reliable speed regulation basis for the permanent-magnet direct-drive belt conveyor system and also provides a theoretical reference for energy savings and consumption reduction in the coal industry.

Coal Flowability Control on Preventing Spillage on Conveyor Belt Through Modeling and Simulation for Improving its Performance

Belt conveyors are generally used in mining plant areas, both surface and underground mines. The belt conveyor is mainly applied to transport the extracted bulk material from the mining site to delivery. The effectiveness of the extraction process depends on the reliability and durability of the conveyor belt system. In addition, conveyor performance is very important specially to control material flowability to prevent spills or other operational disturbances to optimize production throughput. However, the transfer chute and settling zone can cause some problems during the transfer process, such as material spills. This problem can reduce the function and performance of the conveyor belt. This paper discusses a design model to reduce the problem of spillage in the settling zone. The model was developed by compiling the previous defecting data from the durability of the conveyor system, then analyzed using Discrete Element Method (DEM) software and compared with bulk characteristics. The initial performance of certain conveyors is only capable of serving with an average production of 76% of the designed capacity while energy is consumed at full load. By applying the DEM simulation result, the blade gate can reduce the peak angle break in the depositional zone before exiting. After the analysis is completed using DEM, the conveyor increases the average production to 95% of the designed capacity. In conclusion, controlling the maximum belt load without spillage will reduce interruption on conveyor belt operation and maintenance costs therefore increase plant reliability and availability.

Cover Picture: Geomechanics and Tunnelling 6/2021

AbstractThe Borth underground salt mine with an annual production volume in the range of 2 m t of halite is amongst the largest underground mines in Central Europe. All mined halite is transported to the central hoisting shaft with belt conveyor systems. To ensure a safe and efficient operation of the main conveyor belt, precautionary support measures in geotechnically stressed areas of various stopes were necessary. For the rehabilitation measures, an innovative anchoring system was applied using 3.7 to 5.7 m long hollow bar anchors which were first anchored mechanically in a borehole, pre‐stressed and then fully grouted with resin.

Towards accurate non-contact moisture inspection using THz imaging and thickness information

This study proposes an approach for a non-contact moisture inspection in dried food products, which is crucial to maintain optimal quality and shelf-life, using terahertz (THz) signal. To achieve this, a sample-specific calibration curve needs to be determined first. HAITAI crackers were chosen in this work for demonstration purposes. Fifteen stacks of crackers with different heights were prepared and moisturized by covering with a wet tissue paper for different time periods, resulting in moisture levels between 3 and 40% R.H.. Then, each sample was placed on a conveyor belt system between a THz source and THz a detector, and transmitted signal was measured 5 times. After that, moisture percentage of the sample was determined based on a gravimetric method, whose results served as a ground-truth measurement. A thickness of the sample was also measured using a vernier. All signal measurements, together with their corresponding known thicknesses and moisture percentages, were used to calculate necessary coefficients that define a sample-specific calibration curve. Once a calibration curve for the cracker was obtained, it was used to estimate the moisture percentages in samples with different thicknesses. Mean absolute error (MAE) of the moisture percentage is found to be less than 12% when the sample thickness is modelled as part of the calibration curve, which is over 50 times less compared to the case when the sample thickness is not modelled. Therefore, the utilization of an automatic thickness determination would be promising for real-time and accurate non-contact moisture inspection. This approach can be also integrated into a production line to improve quality control in the food industry without interrupting existing processes.

Investigation in characteristics of belt conveyors and their idler rollers for coal-fired power plants

Belt conveyors are highly efficient systems used in mining and other industries to transport bulk materials, mainly brown coal and overburden, over distances ranging from short distances to several kilometres. For technical reasons, their operation is associated with noise emissions. During inspections and maintenance this can lead to increased noise exposure of employees. In the vicinity of residential buildings or in areas needing protection, exceeding the emission limits can lead to a temporary restriction of the operation of these high performance installations. Measures are required to reduce the noise impact primarily at the source or in the immediate vicinity. The acoustic and mechanical parameters are investigated on a test bench at the Technical University of Brandenburg (BTU) Cottbus-Senftenberg as part of the drive assembly tests in cooperation with LEAG. The stages of these tests are: Run-up test. In this test the supporting roller is accelerated under test load to a circumferential speed of 10 m/s and the sound pressure level is recorded. This makes it possible to calculate the sound emission as a function of the peripheral speed; Recording the geometry of the idler gear. An accurate description of the geometry of the idler housing allows conclusions to be drawn about the possible causes of excitation. The defined characteristics serve to quantify the acoustic effect of the driven rollers and derive specifications for use in belt conveyor systems. Based on the special requirements to the planned belt conveyor system, an adapted selection of low-noise idler rollers can be made at an early stage on the basis of specified parameters and confirmed test results.

Control Algorithm of Permanent Magnet Direct Drive Belt Conveyor System for Mining Based on Reduced Order Model

Over the decades, permanent magnet synchronous motor (PMSM) has been widely used in coal mine production. In this paper, an optimized neural network predictive controller (NNPC) of permanent magnet direct drive belt conveyor system (BCS) for mining based on reduced order model (ROM) is established. First, in order to establish the full order model of the permanent magnet direct drive BCS, CEMA is used for dynamic analysis, and the dynamic equation of the permanent magnet direct drive BCS is established. Second, the Proper Orthogonal Decomposition (POD) method is used to reduce the order in this paper. Finally, the NNPC of permanent magnet direct drive BCS based on the ROM is proposed. The simulation result shows that the order of BCS model is effectively reduced by the POD method. The NNPC based on the ROM has a good performance in the control of permanent magnet direct drive BCS, and the error between of the full order model and the ROM is 0.19[Formula: see text]m/s.

Establishing failure patterns of a Belt Conveyor System configuration

Abstract: The conveyor belt is one of the most operational critical equipment’s in the mining industry, they are mostly used in the transportation of crushed materials from the crushing station to where there’ll be further processed. Due to the increasing complexity of belt conveyor systems, managing their integrity has become even more difficult, as they are now used across various industries, environments and carry materials of different weight variations, leaving them susceptible to failures (1). This paper provides an industry specific knowledge on belt conveyor systems, their respective components, and how they are configured using fault tree analysis to predict the different branches of event that can contribute to the failure of a belt conveyor system. The use of fault tree analysis sheds more light on how cascading failures can occur, where the failure of one component leads to the failure of the overall system. (2) Keywords: RCFA, FMEA Opex, FTA, Capex, Eca, Ttf, Ttr.

Failure Rate Prediction of Belt Conveyor Systems using 2-parameter Weibull distribution

Abstract: The conveyor belt is one of the most operational critical equipment’s in the mining industry, they are mostly used in the transportation of crushed materials from the crushing station to where there’ll be further processed. Due to the increasing complexity of belt conveyor systems, managing their integrity has become even more difficult, as they are now used across various industries, environments and carry materials of different weight variations, leaving them susceptible to failures (1). This paper provides an industry specific knowledge on how Weibull analysis can be used to predict the failure rate of a conveyor belt system, using parameters such as the time to failure (TTF), installation and failure dates, as determinant parameters for the predictions. Several Weibull failure distributions and functions have been used to establish accuracy of results and to create comparisons on the different ways in which risk, unreliability and availability are quantified, using calculated values such as the Shape and scale parameter. The paper utilizes real world case studies in the area of mining, which sheds light on key component failures and their cut sets within the conveyor belt system (2) Keywords: TTF, TTR, Threshold parameter, Repair date, Shape parameter, B10, B15, B20, Scale parameter, ECA, CDF, PDF

Development of a framework for safety performance measurement of belt conveyor systems

PurposeThe purpose of this paper is to develop a framework for the safety performance measurement of belt conveyor systems.Design/methodology/approachA structural methodology of graph theory and matrix approach is used for developing a framework for safety performance measurement of belt conveyor systems.FindingsThe development of a framework for safety performance measurement of belt conveyor systems is essential for ensuring plant safety. For this, safety performance factors, including design and operating contextual factors of belt conveyor systems, are identified. The factors along with their interrelations are modeled using digraph. An equivalent matrix of the digraph provided safety performance function (SPF) of belt conveyor systems, leading to the development of a safety performance index (SPI).Practical implicationsThe developed framework will enable the designers for evaluating and comparing alternative designs of conveyor systems from the safety viewpoint. The plant operators can make inferences from the SPI to identify the weak contextual factors in the plant and develop action plans for its mitigation.Originality/valueThe paper is novel and employs graph theory and matrix approach for safety performance measurement. The methodology helps in the quantitative evaluation of the safety performance of belt conveyor systems.

Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms

In this study, a sensor tip with a metallic hemispherical nozzle tip (MHNT) design based on the Fabry-Perot interferometer was developed for surface roughness recognition (SRR). Sandpaper samples with ten different arithmetical mean deviations of the surface (Sa) values were used as surfaces to be recognized. The feature vectors were found by applying the discrete wavelet transform (DWT) to the analog signals obtained from the sandpaper samples. Machine learning (ML) algorithms K-nearest neighbor (KNN) and support vector machine (SVM) were used for classification. An in-depth recognition process was carried out using the classifiers’ different length criteria and kernel types. In the test process, each category consists of two sub-categories as testing within the training dataset (TWITD) and testing without the training dataset (TWOTD). The experiments were carried out in a controlled manner with the conveyor belt system (CBS) and manual. As a result of the experimental studies, the average recognition rates (Rave) for CBS were found as 84.2% and 81.6% for TWITD and TWOTD, while the Rave for the manual are found as 80% and 77.5% for TWITD and TWOTD, respectively.

Investigation of transient multiphase Taylor-Couette flow

A multiphase, transient, horizontal, Taylor-Couette flow has been investigated numerically. This flow governs thermal performance of self-contained drum motor (SCDM) drive systems. These drive systems are extensively used with conveyor belt systems, employed in many industrial and commercial applications. Thermal performance of the SCDM drive system is governed by heat transfer within multiphase flow of air and oil. This flow is confined within a horizontal annular space between two concentric cylinders. The outer cylinder is the drum. It rotates and rejects heat to the atmosphere. The inner cylinder is the outer surface of the motor. It is stationary and subjected to a constant heat flux. This heat is generated within the electric motor, gearbox, and oil viscous dissipation. Numerical simulations have been carried out using ANSYS-CFX. Transient flow and thermal fields have been investigated, starting from an initial stationary state until a quasi-steady state is reached. The effect of drum rotational speed and oil level on thermal performance of the SCDM drive system has been investigated. The study covers a range of rotational speeds from 0 to 150 rpm, oil level from 0%, (i.e., 100% air-filled) to 100%. Numerical results have been validated using experimental data. The optimum oil level has been determined which gives the lowest possible overall maximum SCDM system temperatures. This optimum oil level depends on the drum rotational speed. Results indicated that the 100% oil-filled case does not provide the best thermal performance. The Nusselt number around the inner cylinder has been correlated as a function of rotational Reynolds number and oil level. The developed correlation can be used as a design tool. This tool is to optimize system thermal performance by determining the optimum oil level at a given rotational speed.

Optimization of the cross section of a novel rail running conveyor system

The throughput of a belt conveyor system is the primary design parameter when considering a new installation, determined by the cross-section of the material on the belt, coupled with the belt speed. Optimizing this area not only improves efficiency, but also minimizes capital costs through the optimal selection of equipment. Whilst speed-related optimization has seen considerable attention, the cross-sectional area has largely been neglected due to existing design constraints of the system. Conventional belt conveyors typically utilize a 3-idler troughing configuration, which forms a trapezoidal cross-section with a parabolic surcharge. The rigidity of this support directly limits the geometry of the cross-section that may be considered. A new conveyor system developed at the University of Newcastle supports the conveyor belt by a rail-based carriage, with no relative movement between the belt and carriage. This configuration allows the cross-section of the belt to be freely optimized in order to maximize the material throughput for a given belt width, or alternatively to minimize the belt width for a given throughput. This article utilizes the calculus of variations to optimize the form of this cross section, and demonstrates that an increase in throughput of up to 30% is possible, compared to troughed installations.

PROTOTYPE MODUL LATIH PNEUMATIC DAN MOTOR DC BERBASIS PLC

Development to realize basic technical competencies that are relevant to the conditions that will be faced in the world of work. It is necessary for teaching media or practicum media that can support and increase students' knowledge and insight regarding the technical application of these courses in the real world, especially in industry. Courses that study pneumatic systems and Programmable Logic Control (PLC) require practical media that can simulate industrial automation processes. In this paper, we will present the design of pneumatic modulation and dc motors that can be used as pneumatic system simulators and dc motors with PLC controllers. This experimental research includes the following stages: planning, manufacture, testing and finalization. This training module is designed to be portable using lightweight and easy-to-obtain materials, and is suitable for PLC learning simulations. Automated systems are represented by electro-pneumatic systems, which can apply industrial processes such as goods propulsion systems, belt conveyor systems and goods chopping systems.

A COMPARATIVE STUDY ON POWER CALCULATION METHODS FOR CONVEYOR BELTS IN MINING INDUSTRY

ABSTRACT This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS’s operational features, the power consumption (Pc, kW) was measured directly on motorised head-pulleys. The Pc was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the Pc.

Logistic challenges at contract SBT 1.1 – Intermediate construction access Göstritz

AbstractBase tunnels usually require temporary intermediate construction accesses in order to be able to carry out the tunnelling work in a timely, economical and logistical reasonable scope. Intermediate construction accesses in the form of shafts lead to particular logistical challenges in supplying the tunnel headings. At contract SBT 1.1‐Tunnel Gloggnitz as the eastern section of the Semmering Base Tunnel, the shafts of the intermediate construction access Göstritz cannot be sunk from the surface for topographical reasons but are instead developed by an access tunnel. This fact requires the construction of complex underground structures and logistical interfaces to supply the four tunnel headings, beginning from the building site facilities, via the access tunnel, through the shafts to the tunnel face. In addition to the ongoing supply of all kinds of support measures, the excavated material is mucked by means of fully automatic belt and shaft conveyor systems. To ensure the permanent safety of the crew on‐site, the highest demands and resilience are placed on the transport systems through the two 250 m deep shafts, the ventilation and the mountain water drainage systems of up to 500 l/s. The article describes the experience and ongoing optimization of the logistics concept as well as innovative new developments that are applied in this special tunnel project.

Research on Power Consumption Model of Permanent Magnet Direct Drive Belt Conveyor System Based on Fruit Fly Optimization Algorithm-Generalized Regression Neural Network-Particle Swarm Optimization

The permanent magnet direct drive electric drum is used to replace the driving device of the traditional belt conveyor, which simplifies the structure. However, the permanent magnet direct drive electric drum still operates in a high speed and stable state after starting, and cannot transport materials reasonably, belt speed cannot match the coal quantity reasonably; the problem of energy waste is still severe. Thus, based on fruit fly optimization algorithm-generalized regression neural network-particle swarm optimization algorithm, the power consumption network model of the permanent magnet direct drive belt conveyor system is established. The relationship among belt velocity, coal transport quantity and power is obtained, and the optimal belt velocity is found with the power consumption network model. Thus, the minimum power is obtained. The algorithm selects the optimal smoothing factor by fruit fly optimization algorithm, inputs the optimal smoothing factor into generalized regression neural network and establishes the optimal power consumption model. Then establishes the matching relationship of coal quantity, optimal power, and optimal belt speed in the power consumption model by the adaptive weight particle swarm optimization. The model is compared with the network model with smoothing factors of 0.7 and 0.4. The comparisons show that the optimized model performs better, which can be better applied to establish the energy consumption model of the system.