Advanced Process Control of a Flotation Column

Flotation cells a solid-solid separation process based on the physical and chemical properties of the mineral particle surface. The process is widely used in the mineral processing industry for a low grade and finely disseminated ores to recover the valuable mineral. It is also appliedin recycling and solvent extraction process. These are several problems associated withthis process namely: a) Poor product recovery due to inconsistent froth stability b) Lowproduct grade attributed to recover of undesired hydrophilic particles c) High energy andmaintenance costof mechanical agitator d) Poor control of the cell's level. The main objective of this research project is to study a possible control type to improve the level control mainly in advance control process. It definitely will enhance the recovery and purity of precious mineral from the ores. There were 5 control types being evaluated in this study which are feedback, feed-forward, cascade, smith predictor and fuzzy logic control. The research work began with developing model of the flotation column process using simulink toolkit within MATLAB software. It is succeeded with development of the abovementioned controlleronto the process model. For those control that uses conventional PID algorithm, similar tuning constant were applied. Each of the control types were subjected to set point change on the level and disturbance (.i.e. ratio of valuable and waste within the feed). The performance for every control type was evaluated and trends were compared. In conclusion, cascade control provides best performance both performance in set pointchange and rejecting disturbance

Mitigation of environmental hazards of sulfide mineral flotation with an insight into froth stability and flotation performance

Today\u27s major challenges facing the flotation of sulfide minerals involve constant variability in the ore composition; environmental concerns; water scarcity and inefficient plant performance. The present work addresses these challenges faced by the flotation process of complex sulfide ore of Mississippi Valley type with an insight into the froth stability and the flotation performance. The first project in this study was aimed at finding the optimum conditions for the bulk flotation of galena (PbS) and chalcopyrite (CuFeS₂) through Response Surface Methodology (RSM). In the second project, an attempt was made to replace toxic sodium cyanide (NaCN) with the biodegradable chitosan polymer as pyrite depressant. To achieve an optimum flotation performance and froth stability, the third project utilized two types of nanoparticles; silica (SiO₂) and alumina (Al₂O₃) as process aids. The fourth project investigated the impact of water chemistry on the process outcomes in an attempt to replace fresh water with sea water. In the last project, five artificial intelligence (AI) and machine learning (ML) models were employed to model the flotation performance of the ore which will allow the building of intelligent systems that can be used to predict the process outcomes of polymetallic sulfides. It was concluded that chitosan can be successfully used as a biodegradable depressant. Alumina nanoparticles successfully enhanced both froth stability and flotation performance while silica nanoparticles did not. Seawater had a negative effect on both the froth stability and the grade of lead (Pb) and copper (Cu) but it improved the recoveries of both Pb and Cu minerals. Hybrid Neural Fuzzy Interference System (HyFIS) ML model showed the best accuracy to be adopted for automated sulfide ore flotation process in the future --Abstract, page iii

Fuzzy model of the computer integrated decision support and management system in mineral processing

During the research on the subject of computer integrated systems for decision making and management support in mineral processing based on fuzzy logic, realized at the Department of Applied Computing and System Engineering of the Faculty of Mining and Geology, University of Belgrade, for the needs of doctoral thesis of the first author, and wider demands of the mineral industry, the incompleteness of the developed and contemporary computer integrated systems fuzzy models was noticed. The paper presents an original model with the seven staged hierarchical monitoring-management structure, in which the shortcomings of the models utilized today were eliminated

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Decentralized proportional-integral controller based on dynamic decoupling technique using Beckhoff TwinCAT-3.1

An improved technique for the design of decentralized dynamic decoupled proportional-integral (PI) controllers to control many variables of column flotation was developed and implemented in this paper. This work was motivated by challenges when working with multiple inputs and multiple outputs (MIMO) systems that are not controllable by conventional linear feedback controllers. Conventional feedback control design consists of various drawbacks when it comes to complex industrial processes. The introduction of decentralization, decoupling, and many advanced controls design methods overcomes these drawbacks. Hence, the design and implementation of control systems that mitigate stability for MIMO systems are important. The developed closed-loop model of the flotation process is implemented in a real-time platform using TwinCAT 3.1 automation software and CX5020 Beckhoff programmable logic controllers (PLC) through the model transformation technique. The reasons for using the CX5020 as an implementation environment were motivated by the reliability, and is built according to new industry standards, allowing transformation, which makes it more advantageous to be used more than any other PLCs. This is done to validate the effectiveness of the recommended technique and prove its usability for any multivariable system. Comparable numerical results are presented, and they imply that industrial usage of this method is highly recommended

Decentralised PI controller design based on dynamic interaction decoupling in the closed-loop behaviour of a flotation process

An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closed-loop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented

Advanced Process Control of a Flotation Column

Flotation cells a solid-solid separation process based on the physical and chemical properties of the mineral particle surface. The process is widely used in the mineral processing industry for a low grade and finely disseminated ores to recover the valuable mineral. It is also appliedin recycling and solvent extraction process. These are several problems associated withthis process namely: a) Poor product recovery due to inconsistent froth stability b) Lowproduct grade attributed to recover of undesired hydrophilic particles c) High energy andmaintenance costof mechanical agitator d) Poor control of the cell's level. The main objective of this research project is to study a possible control type to improve the level control mainly in advance control process. It definitely will enhance the recovery and purity of precious mineral from the ores. There were 5 control types being evaluated in this study which are feedback, feed-forward, cascade, smith predictor and fuzzy logic control. The research work began with developing model of the flotation column process using simulink toolkit within MATLAB software. It is succeeded with development of the abovementioned controlleronto the process model. For those control that uses conventional PID algorithm, similar tuning constant were applied. Each of the control types were subjected to set point change on the level and disturbance (.i.e. ratio of valuable and waste within the feed). The performance for every control type was evaluated and trends were compared. In conclusion, cascade control provides best performance both performance in set pointchange and rejecting disturbance

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