The specific selection function effect on clinker grinding efficiency in a dry batch ball mill

Dry grinding experiments on cement clinker were carried out using a laboratory batch ball mill equipped with a torque measurement. The influence of the ball size distribution on the specific selection function can be approached by laboratory runs using mono-size balls. The breakage is more efficient with maximal specific selection functions at the initial size reduction stage. But, in terms of cement finish grinding all stages of grinding are determinant for the production of a required Blaine surface area (3500 cm2/g). So, the choice of ball size according to a maximal specific selection function leads to an increase of the energy consumption. In addition, investigations on the mono-sized fractions and on the crude material (size minus 2.8 mm) demonstrate that the energy efficiency factor can be optimized using ball size corresponding to relatively low specific selection function

PBM and DEM simulations of large-scale closed-circuit continuous ball mill of cement clinker

Cement milling is known to be inefficient and energy-intensive. Thus, even small improvements in cement milling\u27s performance could significantly reduce operation costs. This dissertation aims to develop a simulation tool for dry milling and generate a fundamental process understanding, which enables process optimization. To this end, a true unsteady-state simulator (TUSSIM) for continuous dry milling is developed and applied to model various processes: (a) open circuit continuous mills, (b) closed-circuit continuous mills, and (c) vertical roller mills. TUSSIM is based on the solution of the cell-based population balance model (PBM) for continuous milling, which consists of a set of differential algebraic equations (DAEs). Moreover, air classifier parameters and ball size distribution for the closed-circuit operation are tailored to maximize production capacity while achieving desirable cement product qualities. Discrete element method (DEM) and PBM are coupled to simulate lab-scale batch milling of cement clinker to gain fundamental understanding of the roles of ball size and material (steel vs. alumina). First, dynamic simulations are performed to investigate the impact of ball mill operation parameters on the full-scale open-circuit ball milling of cement clinker without an external air classifier. Parameters for the simulation are taken from the literature. Simulation results suggest that a single-compartment mill produces the desired cement size, but it requires pre-milled fresh feed. Depending on the ball sizes used, a two compartment mill produces cement sizes similar to those produced by a three-compartment mill. A uniform mass of balls achieves an 8% higher specific surface area (SSA) compared to a uniform number of balls. The classifying liners have negligibly finer cement products compared to a uniform mass distribution. TUSSIM is also incorporated with a variable Tromp curve model for classification to simulate full-scale closed-circuit ball milling with an air classifier. The simulation results suggest that a faster rotor speed or lower air flow rate leads to a finer cement product and increases the dust load of the classifier feed. Integrating air classifiers into open-circuit ball milling increases the production rate by 15% or cement SSA by 13%. Operation failure due to overloading of the entire circuit is detected when dust load is too high. Process optimization with a global optimizer?DAE solver is performed to identify either the air classifier\u27s parameters or the ball size distributions that yield desirable cement quality while maximizing production rate. Optimization results show that the production rate can be increased by 7% compared to the baseline process. Unlike open circuits, a two-compartment mill produces a finer cement product than a three-compartment mill. Optimal ball mixtures are identified in a two-compartment mill, suggesting a 14% increase in production rate at a desirable cement quality. A global optimizer-based back-calculation method, based on PBM, is used to determine the breakage kinetics parameters of cement clinker in a lab-scale ball mill loaded with steel or alumina balls of three single ball sizes and their mixtures. The motion of the balls in the mill is simulated via the DEM. The results show that steel balls achieve faster breakage of clinker than alumina balls, which is explained by the higher total?mean energy dissipation rates of the steel balls. The particle size distribution (PSD) becomes finer as smaller balls are used. The ball mixture is the most effective overall. Significant energy can be saved if steel balls are replaced with alumina balls, but the slower breakage with the alumina balls needs to be accounted for. Finally, steady-state cement PSD obtained from a full-scale vertical roller mill is fitted with TUSSIM. The fitted results show good agreement compared to the experimental PSD. Overall, this dissertation has provided a novel process simulator, TUSSIM, and many fundamental insights into the continuous milling of cement clinker and its optimization

Recovery of materials from recycling of spent furnace linings

The objective of this research study is to evaluate the technical feasibility of liberating metal entrapped in the spent melting furnace linings obtained from a non-ferrous metal producer and develop an economic technique to recycle all of the materials presently landfilled. Five to six million pounds of spent melting furnace linings are landfilled annually from this non-ferrous producer --Abstract, page iii

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

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.project LifeSaver - Context sensitive monitoring of energy consumption to support energy savings and emissions trading in industry (G.A. FP7-ICT-287652

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

© 2021 by the authors. Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant

Efficient modeling and control of crushing processes in minerals processing

Modeling and simulation is a tool to explore and increase the understanding of a phenomenon. This thesis focuses on developing models of crushers and equipment used in the mining industry. Specifically, the focus is on a branch of modeling called time dynamic modeling which is a model that gives an output as a function of time. The work is divided into three areas: physical modeling, control modeling, and circuit modeling. Physical modeling deals with how to develop high fidelity unit models of equipment, in this thesis, a model of a jaw crusher and of an HPGR are presented. These models are aimed to be predictive and should predict the process variables under a specific set of operating conditions. The models are developed with the process parameters that are used in the physical unit, in the case of the HPGR, roller speed, and hydraulic pressure. The parameters within the models are parameters with units and have real physical meaning; for example, a dimension of the machine. The topic of control modeling focuses on how to apply the knowledge from modeling in the control domain to improve operations. An example of setting up a model predictive controller and using it to control a crushing circuit simulation is demonstrated. Model predictive control is an optimal control strategy that can be used to drive the circuit towards a specific goal. As the demand is increased on the mining companies to perform better these types of controllers and operation improving actions are important. This thesis aims to target some of the challenges involved in improving plant operation and control. Within circuit modeling, a broader perspective is taken to study the operations of an entire circuit or plant. The study presented in this thesis focuses on how sensitive a plant is to variations and how the plant design itself will affect the plant\u27s ability to cope with variations. The approach has been to simulate faster and to use less complex models many times to determine limits and ranges. The method shows potential to understand a circuit better before it is built.The outcome of the research is a better understanding of how to model machinery, such as the HPGR and the jaw crusher. By developing high fidelity models, insights are gained on how to move between the different modeling domains. The knowledge is useful for studies of circuits, and how to set up optimal controllers. Especially controllers that require models of a specific type or models that have to be fast to simulate

Predictive Control of a Closed Grinding Circuit System in Cement Industry

This paper presents the development of a non-linear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution (PSD) of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller in order to manage distributed controllers (DCs) installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work