Applied Calibration and Validation Method of Dynamic Process Simulation for Crushing Plants

There is a need within the production industry for digitalization and the development of meaningful functionality for production operation. One such industry is aggregate production, characterized by continuous production operation, where the digital transformation can bring operational adaptability to customer demand. Dynamic process simulations have the ability to capture the change in production performance of aggregate production over time. However, there is a need to develop cost-efficient methodologies to integrate calibrations and validation of models. This paper presents a method of integrating an experimental and data-driven approach for calibration and validation for crushing plant equipment and a process model. The method uses an error minimization optimization formulation to calibrate the equipment models, followed by the validation of the process model. The paper discusses various details such as experimental calibration procedure, applied error functions, optimization problem formulation, and the future development needed to completely realize the procedure for industrial use. The validated simulation model can be used for performing process planning and process optimization activities for the crushing plant’s operation

Application of Optimization Method for Calibration and Maintenance of Power-Based Belt Scale

Process optimization and improvement strategies applied in a crushing plant are coupled with the measurement of such improvements, and one of the indicators for improvements is the mass flow at different parts of the circuit. The estimation of the mass flow using conveyor belt power consumption allows for a cost-effective solution. The principle behind the estimation is that the power draw from a conveyor belt is dependent on the load on the conveyor, conveyor speed, geometrical design, and overall efficiency of the conveyor. Calibration of the power-based belt scale is carried out periodically to ensure the accuracy of the measurement. In practical implementation, certain conveyors are not directly accessible for calibration to the physical measurement as these conveyors have limited access or it is too costly to interrupt the ongoing production process. For addressing this limitation, a better strategy is needed to calibrate the efficiency of the power-based belt scale and maintain the reliability of such a system. This paper presents the application of an optimization method for a data collection system to calibrate and maintain accurate mass flow estimation. This includes calibration of variables such as the efficiency of the power-based belt scale. The optimization method uses an error minimization optimization formulation together with the mass balancing of the crushing plant to determine the efficiency of accessible and non-accessible conveyors. Furthermore, a correlation matrix is developed to monitor and detect deviations in the estimation for the mass flow. The methods are applied and discussed for operational data from a full-scale crushing plant

Rapid Test to Establish Grading of Unbound Aggregate Products: An Evaluation of Automated Devices to Replace and Augment Manual Sieve Analyses in Determining Aggregation Gradation

Several automated devices are commercially available for measuring the gradation of unbound stone aggregates. These computerized machines, which provide a rapid alternative to manual sieving, capture and process two-dimensional digital images of aggregate particles to determine grain size distribution. Five of these automated gradation devices were evaluated for accuracy and performance. Fifteen aggregate test samples, with different size, shape, and mineral characteristics, were used in these tests. To quantify how well the machine results compare with data from standard sieve analyses, the CANWE statistic was developed and used. While the machine data did not match the sieve data exactly, the evaluated devices were found to provide good measures of particle gradation for most samples. These tests also indicate that some machines will give more repeatable results in multiple tests of a given material, while others yield better results when testing different materials. The methodology used in this study is suitable for objectively evaluating the accuracy of other rapid gradation machines for various applications. ICAR Project 503 was undertaken to study rapid, automated methods of determining the grain size distribution of unbound aggregate products. Two technologies were studied in detail: digital image analysis and laser profiling. This report summarizes the evaluation of digital imaging devices, while the second part of the final project report describes the development of a laser scanning device for grading aggregates.Aggregates Foundation for Technology, Research, and Education (AFTRE)Civil, Architectural, and Environmental Engineerin

Energy-Efficiency of Conveyor Belts in Raw Materials Industry

This book focuses on research related to the energy efficiency of conveyor transportation. The solutions presented in the Special Issue have an impact on optimizing, and thus reducing, the costs of energy consumption by belt conveyors. This is due, inter alia, to the use of better materials for conveyor belts, which reduce its rolling resistance and noise, and improve its ability to adsorb the impact energy from the material falling on the belt. The use of mobile robots designed to detect defects in the conveyor's components makes the conveyor operation safer, and means that the conveyor works for longer and there are no unplanned stops due to damage

Optimization Capabilities for Crushing Plants

Responsible production and minimal consumption of resources are becoming competitive factors in the industry. The aggregates and minerals processing industries consist of multiple heavy mechanized industrial processes handling large volumes of materials and are energy-intensive. One such process is a crushing plant operation consisting of rock size reduction (comminution) and particle size separation (classification) processes. The objective of the crushing plant operation for the aggregates industry is to supply specific size fractions of rock material for infrastructure development, while the objective in minerals processing is to maximize material ore throughput below a target size fraction for the subsequent process. The operation of a crushing plant is complex and suffers variabilities during the process operation, resulting in a drive for optimization functionality development. Process knowledge and understanding are needed to make proactive decisions to enable operations to maintain and elevate performance levels. To examine the complex relationships and interdependencies of the physical processes of crushing plants, a simulation platform can be used at the design stage. Process simulation for crushing plants can be classified as either steady-state simulation or dynamic simulation. The steady-state simulation models are based on instantaneous mass balancing while the dynamic simulation models can capture the process change over time due to non-ideal operating conditions. Both simulation types can replicate the process performance at different fidelities for industrial applications but are limited in application for everyday operation. Most companies operating crushing plants are equipped with digital data-collection systems capturing continuous production data such as mass flow and power draw. The use of the production data for the daily decision-making process is still not utilized to its full potential. There are opportunities to integrate optimization functions with the simulation platform and digital data platforms to create decision-making functionality for everyday operation in a crushing plant. This thesis presents a multi-layered modular framework for the development of the optimization capabilities in a crushing plant aimed at achieving process optimization and process improvements. The optimization capabilities for crushing plants comprise a system solution with the two-fold application of 1) Utilizing the simulation platform for identification and exploration of operational settings based on the stakeholder’s need to generate knowledge about the process operation, 2) Assuring the reliability of the equipment model and production data to create validated process simulations that can be utilized for process optimization and performance improvements.During the iterative development work, multiple optimization methods such as multi-objective optimization (MOO) and multi-disciplinary optimization (MDO) are applied for process optimization. An adaptation of the ISO 22400 standard for the aggregates production process is performed and applied in dynamic simulations of crushing plants. A detailed optimization method for calibration and validation of process simulation and production data, especially for mass flow data, is presented. Standard optimization problem formulations for each of the applications are demonstrated, which is essential for the replicability of the application. The proposed framework poses a challenge in the future development of a large-scale integrated digital solution for realizing the potential of production data, simulation, and optimization. In conclusion, optimization capabilities are essential for the modernization of the decision-making process in crushing plant operations

Advanced Techniques and Efficiency Assessment of Mechanical Processing

Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue

Process Control of Crushing Circuits

Kivenmurskaus on keskeinen osaprosessi kiviaineksen, metallien ja sementin tuotannossa. Murskaamalla tuotetut raaka-aineet muodostavat nykyaikaisen infrastruktuurimme perustan. Huolimatta merkittävästä roolistaan, kivenmurskaus on yksi harvoista teollisista prosesseista, jonka prosessinohjaus toteutetaan edelleen kokemusperäisesti, ilman luotettavaa mittaustietoa suoritettujen ohjaustoimien vaikutuksista. Nykykäytäntö altistaa murskausprosessit prosessivaihteluille ja –häiriöille, ja johtaa viime kädessä tehottomaan tuotantoon ja kapasiteetin vajaakäyttöön. Pääsyinä nykytilaan voidaan pitää murskausprosessien puutteellista anturointia ja tutkimustiedon puutetta korkeamman automaatioasteen tuomista hyödyistä. Tässä väitöskirjassa pyrittiin ratkaisemaan edellä mainittu ongelma automaattisen prosessinohjauksen avulla. Päätavoitteena oli kehittää säätömenetelmät murskauspiirin suorituskyvyn saattamiseksi lähelle parasta saavutettavissa olevaa tasoa. Tämä tutkimus perustuu mallipohjaiseen säädönsuunnittelumenetelmään. Systemaattinen suunnitteluprosessi alkoi säätötavoitteiden määrittelystä ja dynaamisten prosessimallien kehittämisestä. Kehitettyjen prosessimallien avulla luotiin säätötavoitteet täyttävä säätöstrategia ja viritettiin strategian vaatimat prosessisäätimet. Lopuksi simulointimallien avulla kehitetty ja testattu säätöstrategia implementoitiin osaksi laitoksen automaatiojärjestelmää ja sen suorituskyky arvioitiin täyden mittakaavan prosessikokeiden avulla. Tämä väitöskirja on osoittanut, että murskauspiirin tehokas ja tarkoituksenmukainen toiminta vaatii eri kahden säätötavan toteuttamista: massataseen säätö ja hienonnusmäärän säätö. Massatasesäädön tavoitteena on varmistaa 100 % käyttöaste murskauspiirin pullonkaulassa. Hienonnusmäärän säätö varmistaa halutun murskaimen tuotemateriaalin partikkelikokojakauman. Kehitetyt hienonnusmäärän säätömenetelmät perustuvat itseoptimoituvaan säätötapaan, joka mahdollistaa likimain optimaalisen suorituskyvyn käyttämällä säätimessä vakio-asetusarvoa. Kun tämä asetusarvo valitaan optimaalisesti, mahdollistaa esitelty ohjausstrategia parhaan saavutettavissa olevan murskauspiirin suorituskyvyn. Työn merkittävä tunnuspiirre on erityisen kattava empiria. Kehitetyt menetelmät testattiin kattavasti useissa erilaisissa tuotantoskenaarioissa ja prosessikonfiguraatioissa. Täyden mittakaavan prosessikokeiden tulokset vastasivat hyvin lähelle simulaatioilla saatuja tuloksia. Tämä väitöskirja on merkittävä edistysaskel murskausprosessien säädössä. Työn tuloksena kehitetyt mittaus- ja säätötavat mahdollistavat tehokkaamman ja tarkoituksenmukaisemman raaka-ainetuotannon. Työn tuloksilla voidaan olettaa olevan merkittävä vaikutus siihen, miten ja millä tavoin murskausprosesseja ohjataan tulevaisuudessa. Työssä kehitetyn murskauspiirin automaattisen säätöstrategian voidaan olettaa toimivan perustana tulevaisuuden murskausprosessien prosessiautomaatio-toteutuksille.Crushing is an essential high-volume processing stage in the production of aggregates, metals and cement. Crushed products form the basis of our modern infrastructure and therefore play a major role in the economic growth and welfare. Despite its significant role in society, crushing is one of the few remaining industrial processes that is currently being operated using belief-based manual control without the possibility to quantify the consequences of performed control actions. This practice makes crushing processes vulnerable to process variation and exposes them to inefficient production and capacity underutilization. The aim of this thesis is to address this deficiency by bridging the gap between theoretically possible and realized crushing circuit performance, by means of automatic process control. This thesis covers the entire model-based control system design procedure – from the formulation of control objectives and development of dynamic process model(s), through the development of control strategy, to the control system implementation and performance evaluation – for crushing circuits. Research has led to significant advances within crushing process measurement and control. Developed methods have been rigorously tested in various production scenarios and circuit flowsheets, using both dynamic simulations and full-scale experiments. Experiments revealed expected behavior with a significant increase in performance. The results have shown that the efficient operation of a crushing circuit requires addressing two control tasks: mass balance control and size reduction control. The objective of mass balance control is to guarantee 100 percent circuit utilization, whereas size reduction control ensures the desired degree of size reduction. The ideal degree of size reduction is determined empirically to maximize the value of the used KPI. The developed control strategy delivers near-maximum circuit performance. This thesis represents a major leap forward in the area of process control of crushing circuits. It has opened entirely new possibilities by making it possible to quantify the instantaneous performance of crushing circuits and by introducing the ability to ensure consistent and efficient long-term production. These major breakthroughs can have a significant impact on how crushing plants will be operated in the future. Developed standard control practice can be expected to serve as a basis for future control system implementations of industrial crushing circuits

Real-Time Optimization of Cone Crushers

Cone crushers are used in the mineral, mining, and aggregate industry for fragmentation and production of rock materials. Cone crusher control systems are widely used for machine protection, wear compensation and, to some extent, increasing production. These systems ordinarily focus on the crusher and not the yield of production process. In this thesis real-time optimization is explored to the control of eccentric speed and on-line CSS adjustment based on information from the process. The objective is to develop theories, models, software and hardware that enable real-time optimization of a single crushing and screening stage. The main hypothesis is that fixed parameters can never be optimal over time because many things in the process vary continuously. The eccentric speed in a cone crusher determines the number of times a material is compressed and thus the particle size distribution of the product. The speed of the crusher is usually fixed since speed change by changing pulleys is a labor intensive activity. By applying a frequency converter to the crusher motor power supply, it is possible to continuously adjust the eccentric speed. The cost for frequency converters has decreased significantly over the last decade. By applying mass-flow sensors to the process, e.g. conveyor-belt scales, the crusher result can be monitored and the result can be fed back to an operator or a computer. To analyze data from the process and automatically calculate the appropriate value for the Closed Side Setting (CSS) and eccentric speed, algorithms have been developed. The goal for the algorithms is to maximize the product yield in a given moment. The algorithms are loaded into computer systems that can communicate with sensors and crushers. The developed algorithms are tested and evolved at full-scale aggregate crushing plants. Crushing stage performance increased 3.5% in terms of production yield compared to a fixed CSS when the algorithm was implemented in addition to the existing control system. The algorithm automatically compensates for changes in the feed material and also decreases the need for calibration of the CSS. The crushing stages where the speed algorithm were tested increased their performance by between 4.2% and 6.9% compared to a good fixed speed. In real life however, the performance was increased by almost 20% since an inappropriate speed was selected during installation. As a bonus, on one of the test plants for the dynamic speed, the lifetime of the manganese wear parts increased 27% on the evaluated crusher, as a consequence of changed crusher dynamics. In conclusion, real-time optimization has been demonstrated to be feasible and increases the production yield with significantly numbers and should thus be of commercial interest to the industry

Applications of Dynamic Modeling in Crushing Plants

Modeling is a tool to describe phenomena in a simplified way, and the models can then be used to simulate these phenomena. Models of equipment used in the mining and aggregate industries can be used for process simulations of the processes in those industries to improve the operations. To study processes and the operation of processes, time dynamic models are a great tool. This thesis focuses on applications of time dynamic modeling in crushing plants. The time dynamic models predict the output of the equipment as a function of time. The work presented within this thesis focuses on three areas; Unit modeling, process modeling, and control modeling.Unit modeling refers to developing models of single processing units, which could be a comminution unit, classification unit, or materials handling unit. The new models presented in this thesis are for jaw crushers, high pressure grinding rolls (HPGR), and storage units (e.g., bin, silo, or stockpile). The developed models are based on the fundamental insight of the physics that happens within the unit. The validity of the models is aimed to be broad and cover many operating points and uses. The models are intended for high fidelity process simulation applications.Process modeling refers to the modeling of many interconnected units, and the modeling presented in this thesis has been done with both high-fidelity unit models and with simplified models. Both high fidelity and simple simulations are demonstrated within the thesis. The simpler models are used to try new concepts of plant design or control and study plant robustness or ability to handle variations. Meanwhile, the high-fidelity models can be used to study topics such as particle size distribution, debottlenecking and specific control issues.Control modeling refers to developing controller models to control plants like those modeled within the process modeling section. Optimal control, such as model predictive control (MPC), relies on models to steer processes optimally relative to some objective. The models within those controllers have been discussed in this thesis. Additionally, being able to move between the various fidelity domains of models is beneficial for this application. In this thesis, multiple new models and methods are presented, along with how they can be applied within the minerals processing and aggregate industry, ultimately improving the efficiency and performance of the industries