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

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

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

A flotation control system to optimise performance using peak air recovery

Automatic control of industrial flotation cells and circuits presents a set of significant challenges due to the number of variables, the sensitivity of flotation cells to variation in these variables and the complexity of predicting flotation performance and/or developing a strategy for optimisation. Air recovery, a measure of froth stability, has been shown to pass through a peak as flotation cell aeration increases. Furthermore, the air rate at which the peak air recovery (PAR) is obtained results in optimal flotation performance, whether improved concentrate grade, recovery or both grade and recovery. Peak air recovery, therefore, presents a clear optimising control strategy for the operation of flotation cells which is generic to all flotation cells regardless of position in the flotation circuit. In this study, a novel control system based on PAR is developed and demonstrated using a large continuous laboratory flotation cell. In this study, a direct search optimisation algorithm based on the GSS (generating set search) methodology was developed using a 70 l continuous flotation cell operating with a two-phase system (surfactant solution and air only). Characterisation of the laboratory system showed that it was stable for up to 6 h and exhibited a reproducible peak in air recovery. A dynamic model of the response of the system with regards to changes in air recovery was developed that allowed simulations of the proposed optimising control system to be carried out. The optimisation algorithm was then applied to the experimental system. The trialled GSS algorithm was shown to find the PAR air rate when starting above, below and at the PAR air rate, and additionally with a disturbance introduced into the system. While the direct search approach can be slow, it is simple and robust. This demonstrates an innovative approach to optimising control for froth flotation and is the first application of froth stability maximisation for flotation control

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

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

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

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

Diseño de un controlador predictivo generalizado multivariable para el control de una celda de flotación tipo columna utilizada en el proceso de recuperación de cobre

La industria extractiva de minerales, en el Perú, es un actor muy importante de desarrollo económico y tecnológico. Destacando el cobre como principal mineral. Es importante, como país minero, desarrollar investigaciones y tecnologías que ayuden a mejorar el proceso de extracción y concentración de minerales. En la industria minera actual, donde se ha conseguido un gran avance en la automatización, aún existen grandes retos y oportunidades de mejorar los procesos en búsqueda de una mejor eficiencia. Una de las etapas más importantes y críticas es la concentración de minerales mediante el uso de celdas de flotación. El control efectivo de este proceso permite obtener niveles adecuados e importantes de grado y recuperación en el concentrado de mineral. En el caso de las celdas de flotación tipo columna, el comportamiento es multivariable y muy dinámico por ser dependiente de los cambios de las características del mineral que se procesa y de las perturbaciones de la planta. Es ampliamente conocido que el control predictivo basado en modelos constituye una poderosa herramienta de control de plantas multivariable que presentan un comportamiento dinámico complejo como en el caso de la celda de flotación tipo columna. Es por esto, que el objetivo en la tesis es diseñar un controlador predictivo generalizado (GPC) multivariable para el control efectivo de una celda de flotación tipo columna utilizada en el proceso de recuperación de cobre. El modelo matemático obtenido tiene un grado de aceptación FIT superior a 80%. Se realizaron evaluaciones comparativas del sistema de control de la celda de flotación tipo columna, con los controladores GPC multivariable y PI diseñados considerando diferentes escenarios de operación e índices de desempeño. Se determinó que, el mejor desempeño del sistema de control se obtiene cuando se aplica el controlador GPC multivariable diseñado.Tesi