Paste Thickener Research Articles

Controlling Partially Observed Industrial System Based on Offline Reinforcement Learning—A Case Study of Paste Thickener

Controlling Partially Observed Industrial System Based on Offline Reinforcement Learning—A Case Study of Paste Thickener

Automatic Synthesis of Containerized Industrial Cyber-Physical Systems: A Case Study

Industrial Cyber-Physical Systems (ICPSs) are widely regarded as the next generation industrial control systems and as one of the core technologies of the ongoing fourth industrial revolution. Despite its advantages, ICPSs are heavily dependent on the underlying physical process and their synthesis is a customized effort, demanding in terms of resources, which if not conducted carefully may impact the performance of the system. This work proposes a methodology to tackle ICPS synthesis in a systematic way, by using a set of industrial agents that take as input an standardized process description file and automatically deploy a modular ICPS from pre-designed functional containers. Concrete examples on a tanks system and an industrial paste thickener are presented to illustrate the potential of the proposed methodology.

Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

It is crucial to predict the outputs of a thickening system, including the underflow concentration (UC) and mud pressure, for optimal control of the process. The proliferation of industrial sensors and the availability of thickening-system data make this possible. However, the unique properties of thickening systems, such as the non-linearities, long-time delays, partially observed data, and continuous time evolution pose challenges on building data-driven predictive models. To address the above challenges, we establish an integrated, deep-learning, continuous time network structure that consists of a sequential encoder, a state decoder, and a derivative module to learn the deterministic state space model from thickening systems. Using a case study, we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results. The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories. The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

A Case Study on Sustainable Iron Ore Tailing Management Using Paste Technology

Indian mining industries primarily depend upon high-grade fines mainly because of two factors, one is lack of beneficiation infrastructure and high cost of low-grade fines, the second one is lack of proper tail disposal system. Due to lapses in our policies, miners get better premium by selling high-grade fines over low-grade fines. This restricts the flow of low-grade fines into the market. Again tailing disposal is one of the major concerns of beneficiation plants as it requires a lot of space and handling cost. In view of these problems, most of the miners and pellet producers go ahead with direct grinding route only, even if they have a beneficiation facility. This preferred route eliminates the necessity to have a tailing disposal unit, but only at premium. The premium is, higher iron ore fines’ cost and lower downstream efficiency due to associated gangues and slimes in the final slurry fed to filtration unit. This issue can be addressed by finding a midway solution. The solution being coined as partial beneficiation route. This route proposes to beneficiate the feed iron ore fines partially after properly studying the fractional analysis. Amount of tails generated is considerably lower than that of the full beneficiation mode with almost similar slurry concentrate characteristics. This eliminates the need to have a full-fledged tailing pond to some extent. For a 6 MTPA plant, daily tail disposal requirement comes around 700 MT per day. This can be met in two ways. One is filtration other is paste thickener. Filtration has huge capex and opex associated. Also the down times and process challenges are huge. This leaves us with another option i.e. Paste Technology. It enables maximum water recycling, requires small area for storage and provides faster material reclamation and reduces the risk of tailing dam disasters. Paste is a homogeneous mixture of solid and bearing fluid, in our case iron slimes and water. The distinct properties are higher yield stress and solid percentage. This means better storage and uniform drying. The dried slimes can be reclaimed and reused or sold to prospective buyers. Entire cycle is of around 15–20 days. This therefore requires a buffer zone to accommodate around 15,000 MT of paste which can easily be done in an area of 4/5 acres. The operational costs are quiet competitive and lower than the filters. Herein we present our case study on plant scale with a paste thickener. We have chosen the partial beneficiation mode and operated on 24 × 7 basis without having a tailing pond. This surely is a way for future mining operations.

Data analysis of a paste thickener

Abstract The solids content of slurry is typically increased in thickeners. A clean overflow and maximum solids concentration in the underflow are the general targets. The flocculant rate and underflow rate are the two independent variables that are typically used for control. The dependent variables include rake torque, underflow density, overflow turbidity, solids interface level (bed depth), solids inventory (bed pressure), solids settling rate and underflow viscosity. The research problem in question is that the outgoing paste is sometimes difficult to pump. The phenomena leading to this situation are not well known. In the worst-case scenario these phenomena cause clogging in the piping. A data analysis has been done to find the variables that affect and correlate with the pumping problem. The scope of this study covers the measurements from the feed line, thickener and underflow. The goal is to gain better understanding of the phenomena after this phase. The data analysis was done using the paste line pressure difference as a response variable and by dividing the data collected from Yara’s Siilinjärvi mill into two parts: operation areas with high and low pressure difference. The analysis is focused on Thickener 1 due to better availability of measurements. The knowledge of the variables found to influence the pressure difference can be utilized in further development.

Enhanced Big Data Approximating Control of an Industrial Paste Thickener

The Big Data revolution refers to using a large amount of data to improve decision making. In process control applications, the use of big data techniques has been restricted to complementing classical control schemes as model-based or PID approaches. This work focuses on a model-free purely data-driven control strategy known as Big Data approximating control (BDAC), which was recently introduced in the context of process control. In particular, this work proposes two modifications to the classical BDAC formulation and presents a real implementation of the enhanced BDAC technique to a real industrial paste thickener.

Neural Network-Based Model Predictive Control of a Paste Thickener Over an Industrial Internet Platform

This article presents a real implementation of a neural network-based model predictive control scheme (NNMPC) to control an industrial paste thickener. The implementation is done over an Industrial Internet of Things (IIoT) platform designed using the seven layer reference model for IIoT systems. Modeling is achieved using an encoder–decoder with attention recurrent neural network, while MPC search is done using particle swarm optimization. An industrial evaluation is presented, which highlights the set-point tracking and disturbance rejection capabilities of the proposed NNMPC technique.

A Novel Procedure for Performance Assessment of Paste Thickeners

ABSTRACT High stability and low settling rate of phosphate fines in water suspensions encountered the dry area-located mines with serious environmental and technical challenges. The best suggestion for these mines is paste thickener installation for dewatering. In this research, the novel procedure has been proposed for evaluating the potential of paste thickeners in dewatering mining tailings. The case study is Esfordi phosphate complex (Iran). Initially, the selection criteria of a proper flocculant (environmental effects, price, particle settling rate, turbidity of overflow water, and underflow water content) were evaluated for current consuming flocculant (A-27) and 12 other ionic and nonionic flocculants. Then, flocculation performance was ranked based on the type and concentration, applying multiple criteria decision-making (MCDM) techniques of ORESTE, MAPPAC, and ELECTRE. Subsequently, the pilot-scale tests were performed for the selected flocculants and the operational conditions were evaluated for the maximum underflow solid content of 70 wt.%. The results of the present research showed that installation and application of this type of thickener could be effective for the case study.

Mathematical model and factors of paste thickener rake torque

In mining, cement paste backfill technology uses the high concentration tailings that remain after mineral processing to backfill underground stopes. These tailings are prepared by dewatering, mixed with a proportion of cement or other binder, then, after sufficient mixing, the saturated homogenized slurry is pumped or gravity-transported. Therefore, high concentration tailings are a prerequisite for cement paste backfill preparation and high quality backfill. The paste thickener is a key component that shows high efficiency dewatering, and rake shearing greatly affects tailings dewatering performance; therefore, appropriate rake torque design is crucial for efficient paste thickener operation. Rake torque is a key factor in determining the dewatering extent and the rate of flocculation in the networked structure bed in the thickener compression zone. In some industry processes, the problems of rake bogging, rat holing, swirl motion, and insufficient underflow concentration can affect production or seriously damage equipment. All these problems can be attributed to poor rake design or operation. Based on the non-Newtonian characteristics of a mud bed and the correlation of slurry initial shear stress with solids concentration, a torque mathematical model was proposed. The model predicted a rake torque increase with an increase in underflow concentration during thickener operation; the percentage of rake torque variation reached 4.67%. Regression analysis indicates that the percentage of rake torque presents as a polynomial function of the underflow concentration. At the same time, bed height affected rake torque slightly and the rake torque percentage varied within 0.27% when the bed height fluctuated. The predicted effects of underflow concentration and bed height on rake torque are confirmed and compared with 64 h of operational data on thickener operations in a lead-zinc mine.

A study on model predictive control in paste thickeners with rake torque constraint

As a separation process, a paste thickener produces underflow with a high solids concentration. Such underflow leads to a high rake torque which could cause serious operational problems such as underflow blockage, pumping problems and potential donut formation. In this work, a model predictive control approach has been developed to control the underflow solids concentration subject to operational constraints. State observers including linear and extended Kalman filters are studied to determine a cost-effective approach to estimating the solids concentration profile in the paste thickener, which is important for thickener control. A rake torque model is validated with industrial plant data. By utilising the monotonic property of the rake torque model, a linear model predictive control (MPC) approach is developed to deal with the nonlinear constrained control problem. Using an industrial paste thickener as a case study, simulation results demonstrates that the proposed control approach, including an extended Kalman filter, can effectively regulate the underflow solids concentration within permissible operating windows, preventing rake lifting and pumping problems.

Model predictive control with non-uniformly spaced optimization horizon for multi-timescale processes

Many chemical processes exhibit disparate timescale dynamics with strong coupling between fast, moderate and slow variables. To effectively handle this issue, a model predictive control (MPC) scheme with a non-uniformly spaced optimization horizon is proposed in this paper. This approach implements the time intervals that are small in the near future but large in the distant future, allowing the fast, moderate and slow dynamics to be included in the optimization whilst reducing the number of decision variables. A sufficient condition for ensuring stability for the proposed MPC is developed. The proposed approach is demonstrated using a case study of an industrial paste thickener control problem. While the performance of the proposed approach remains similar to a conventional MPC, it reduces the computational complexity significantly.

Model Predictive Control of a Paste Thickener in Coal Handling and Preparation Plants

The control of paste thickener is important because underflow solids concentration has to be maintained within a certain operating window to maximize water recovery while avoiding operational difficulties, e.g. pump bogging. Paste thickener dynamics is complicated due to the interactions between key process variables, uncertainties in operating conditions and large time constant. In this paper, a dynamic model of the paste thickener which addresses varying coal parameters were developed and a model predictive control (MPC) was formulated based on the model. The simulated MPC results based on actual coal processing plant data show that a significant improvement can be achieved in terms of the ability to control the underflow solids concentration as well as conforming to the constraints imposed by the physical limitations of the process, e.g. pump speed and solids amount inside the thickener.

Improving the Removal of Printing Paste Thickener and Excess Dye by Enzymatic Washing of Printed Cotton Fabrics

ABSTRACT In textile printing, it is common to use a printing paste containing a dye, a thickener, Other additives. Thickeners and excess dye must be removed by washing with water after the fixation of the print. This removal is made much more efficient by washing the printed textile in the presence of a suitable enzyme. The present study relates to the removal of CMC thickener by enzymatic washing of cotton fabrics printed using reactive dyes. This enzymatic washing not only improves the quality of the printed cotton fabrics but also decreases the harmful effects of waste water and environmental pollution with thickeners. Also, the enzymatic breakdown of thickener decreases the process time, as well as the amount of energy and water needed to achieve a satisfactory quality of the printed fabrics.

Use of a biomaterial as a thickener for textile ink‐jet printing

AbstractThe feasibility of using chitosan as a thickener in the pretreatment print paste for textile ink‐jet printing was explored. An orthogonal analysis was used to determine the optimum conditions for using chitosan as a thickener in the pretreatment print paste and the effects of different process factors for achieving the best color yield in textile ink‐jet printing. With the help of the orthogonal analysis, the importance of different process factors was found to be in the order of (1) the amount of urea used, (2) the amount of chitosan used, (3) the amount of sodium bicarbonate used, and (4) the steaming time. On the basis of the results of the orthogonal analysis, the optimum conditions for using chitosan as a thickener for the pretreatment print paste were concluded to be 40 mL of chitosan, 10 g of urea, 8 g of sodium bicarbonate, and 5 min of steaming. According to an analysis of the results of different color fastness tests, chitosan could principally work as a pretreatment print paste thickener. However, the final color yield obtained from chitosan‐containing cotton fabrics depended greatly on the stage of the chitosan application. Nevertheless, the color fastness properties and the outline sharpness of the prints of cotton fabric were greatly improved by the chitosan treatment. A two‐bath chitosan treatment was developed to separate the chitosan from sodium bicarbonate and urea before it was padded onto the fabric surface to minimize the neutralization effect. On the basis of the results for the highest color yield obtained on the cotton fabric, it was confirmed that the two‐bath chitosan treatment was successfully developed. In addition, chitosan could impart higher antibacterial properties with a slight reduction in the tensile strength of the cotton fabric. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008