Key Process Variables Research Articles

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.

Using LSS to improve the efficiency and quality of a refund process in a logistics center

Purpose – This study aims to demonstrate a service quality improvement project implemented by the Lean Six Sigma (LSS) methodology to achieve dramatic improvements with respect to service quality and speed in the refund process of the logistics center in Amway Company, Taiwan. It describes in detail how the LSS methodology was applied and how various qualitative and quantitative tools and techniques within the define, measure, analyze, improve and control (DMAIC) logic roadmap have been employed. Design/methodology/approach – This paper adopts the case study methodology to illustrate how to use LSS approach to increase the efficiency of refund process. Findings – The DMAIC scheme with logical analysis, qualitative tools and statistical verification, were employed to identify the key process input variables (KPIVs) that affect the key process output variables (KPOVs) in the refund process. The project team also successfully eliminated non-valued-added (NVA) activities and redesigned the refund process. The most significant improvement is to increase customer satisfaction substantially by reducing the credit voucher waiting time from 14 days to 14 min and the error rate in the refund process is down to almost zero. The results prove that combining the tools of Six Sigma and lean production effects successful improvement of service activities as well as the improvement of manufacturing sectors. Research limitations/implications – The case study method limits the generalization of the findings, but provides a novel way to solve problems for the logistics center. Originality/value – This is one of the rare studies to utilize LSS approach to increase service quality of logistics centers. For the logistics center of Amway Taiwan, the financial benefit of the project was about NT$1,200,000 in annual savings. Numerous invisible gains from the LSS projects are more important for the organization, for example providing customers excellent service, increasing customer satisfaction, training the employees of the company, enhancing the reputation of Amway Taiwan and establishing continuous improvement culture.

Preparation of activated carbons from polycarbonate with chemical activation using response surface methodology

The disposal of waste plastics is a major environmental issue all over the world. As an alternative to disposal that also adds value to the waste product, polycarbonate particles were used as model waste plastic material, mixed with sodium hydroxide and then pyrolyzed at 773 K to produce activated carbon. Activated carbon has numerous industrial applications, including use as adsorbents in adsorption heat pumps and several environmental applications. Activated carbon obtained upon pyrolysis was characterized by determining its adsorption capacity for liquid nitrogen and water vapor. The effects of the key process variables, i.e., chemical ratio and activation time, on micropore development and water adsorptivity were evaluated by response surface methodology. The quadratic models were found to be satisfactory in describing their performance. Based on the contour plots, activated carbon with a maxima of surface area and micropore volumes can be produced at an optimal level of chemical ratio along with longer activation time. The water adsorptivity generally has less difference at low relative pressures, but inflexion of water adsorptivity occurs at a relative pressure of P/P 0 ≈ 0.4. The optimized water adsorptivity in the operating pressure range of adsorption heat pumps (P/P 0 = 0.11–0.38) can exceed 0.24 kg/kg.

Optimization of Plasma Treatment for Enhanced Filler Matrix Adhesion in Manufacturing Green Composites with Rice Husk

The objective of the current work is to study the effect of plasma treatment of rice husk (RH) to reduce the water absorption of natural filler (RH) reinforced unsaturated polyester matrix composite. RH was treated by low-pressure radio frequency plasma in a capacitively coupled plasma reactor at room temperature using nitrogen gas. It was observed that the composites manufactured from plasma-treated RH had lower water absorption as compared with non-treated ones. An experimental matrix for nitrogen plasma treatment of RH with the key process variables such as RF power, treatment time and reactor pressure was determined and experiments were performed according to the experimental matrix to optimize the process parameters by response surface methodology of a central composite design. A quadratic model was developed to predict the percentage reduction of water absorption owing to change in plasma process variables. The process variables were optimized using the predicted model and the optimum conditions were verified by experimental data.

Steam gasification of biomass coupled with lime-based CO2 capture in a dual fluidized bed reactor: A modeling study

Steam gasification of biomass integrated with CO2 capture in a dual fluidized bed with carbon sequestration is among the promising technologies being developed for sustainable production of hydrogen. A simple steady state model which considers two coupled reactors, one calcining limestone particles, while the other steam gasifies biomass and simultaneously carbonates the lime sorbent, is developed in this paper. A stoichiometric equilibrium model is applied for biomass gasification, with enhancement of CO2 removal by carbonation and incomplete conversion of sorbent particles due to kinetic limitations, mixing of the solids and loss of sorbent reactivity because of sintering. To optimize the overall performance of the process, sensitivity analyses are preformed over the most important design and operational parameters. Model predictions are compared with available data from the literature, showing the influence of CO2 capture on gasification. A parametric study reveals the effects of key process variables such as temperature, pressure and solids circulation rate. The model is useful in identifying design and operating conditions for integrated gasification and carbon capture.

Developments in the Production of Galvannealed Steel for Automotive

The state of knowledge of formation of the galvanneal coating on continuous process lines is reviewed. Influences of materials and process variables on phase makeup that are key to achieving acceptability are reviewed. Current targets for key process variables are then described, followed by a consideration of the most frequent galvanneal coating quality issues and their causes. New developments in roll texturing for temper rolling of galvanneal are then described.

Design and optimization of ethanol production from bagasse pith hydrolysate by a thermotolerant yeast Kluyveromyces sp. IIPE453 using response surface methodology

Ethanol production from sugarcane bagasse pith hydrolysate by thermotolerant yeast Kluyveromyces sp. IIPE453 was analyzed using response surface methodology. Variables such as Substrate Concentration, pH, fermentation time and Na2HPO4 concentration were found to influence ethanol production significantly. In a batch fermentation, optimization of key process variables resulted in maximum ethanol concentration of 17.44 g/L which was 88% of the theoretical with specific productivity of 0.36 g/L/h.

Derivation of Postharvest Fruit Behavior Reduced Order Models for Online Monitoring and Control of Quality Parameters During Refrigeration

AbstractIn this paper, an efficient computational tool to predict the evolution of the key variables in the fruit refrigeration process is presented. This computational model is intended to be used in real time quality control.The tool is based on detailed mathematical models proposed in the literature which accounts for the main phenomena occurring during refrigeration. Since integration using standard numerical schemes leads to a computational cost unaffordable in real time environments, a reduced order model (ROM; able to combine a high accuracy with a low computational cost) is derived.In the derivation of this model, the role of the presence of quite disparate time scales is considered. In this respect, two alternative ROMs are considered: a general model (aiming at describing the evolution at arbitrary time scales) and a model assuming a strong coupling between the state variables (as expected in the long term behavior of the system).Practical ApplicationsThe modeling reduction methodology proposed in this work for the fruit refrigeration process, either during storage or transport, will enable the use of computer‐aided process engineering tools for reducing the computational load required to perform predictions. With such tools, optimal operation policies to maximize fruit quality, extend the storage time and/or minimize costs can be efficiently obtained. Also, in the event of unexpected disturbances, they will allow us to recompute in real time the operation policy, and in this way ensure a minimum quality lost.

Development and industrial application of soft sensors with on-line Bayesian model updating strategy

This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables; such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation–maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable.

Monitoring anaerobic sequential batch reactors via fractal analysis of pH time series

Efficient monitoring and control schemes are mandatory in the current operation of biological wastewater treatment plants because they must accomplish more demanding environmental policies. This fact is of particular interest in anaerobic digestion processes where the availability of accurate, inexpensive, and suitable sensors for the on-line monitoring of key process variables remains an open problem nowadays. In particular, this problem is more challenging when dealing with batch processes where the monitoring strategy has to be performed in finite time, which limits the application of current advanced monitoring schemes as those based in the proposal of nonlinear observers (i.e., software sensors). In this article, a fractal time series analysis of pH fluctuations in an anaerobic sequential batch reactor (AnSBR) used for the treatment of tequila vinasses is presented. Results indicated that conventional on-line pH measurements can be correlated with off-line determined key process variables, such as COD, VFA and biogas production via some fractality indexes.

Simplified Model for Description of HVOF NiCr Coating Properties Through Experimental Design and Diagnostic Measurements

The use of factorial design in process parameter development allowed determination of the contribution of key process variables, such as flame energy (combustion pressure and O2/F), spray distance, and feed rate, on in-flight particle properties. The significance of each parameter was used to construct a simple model which enabled the description of particles’ temperature and velocity. Particles with velocities ranging by as much as 300 m/s and temperatures ranging up to 350 °C were used to produce an array of coatings on an in situ curvature sensor enabling the determination of the evolving—during spraying—and residual stress at the end of the process, correspondingly. These diverse particle states combined with the flame impingement on the substrate, resulted in coatings of similar thickness, but significantly different stress states. Real time evolving stresses—during deposition—and coating properties such as, microhardness, modulus, and corrosion behavior were correlated to particle in-flight properties and, via the use of the introduced model, to spray parameters.

Multi-step Functional Process Adjustments to Reduce No-fault-found Product Failures in Service Caused by In-tolerance Faults

This paper proposes a methodology to eliminate or reduce No-Fault-Found (NFF) Product failure in field by adjusting manufacturing processes. No-Fault-Found (NFF) failures can be frequently characterized by in-tolerance faults, i.e., faults occur when design parameters represented by Key Product Characteristics (KPCs) and process variables represented by Key Control Characteristics (KCCs) are within the tolerance limits (in-spec) and therefore they cannot be eliminated by standard process control and adjustment methods. Literature in the area of statistical process and control are unable to monitor and adjust the NFF failures as these methodologies are based on design template itself. Also, all the adjustments strategies consider in process adjustment domain are single-step or adjustments are performed instantaneously. However, the single-step adjustment from process nominal (ud) to functional nominal (uf) is not always possible due to presence of system or resource constraints limiting the amount of adjustment that can be made in a given time such scenario in this paper is refer as multi-step adjustment scenario. This paper proposes a methodology for the multi-step functional process adjustment by identifying the adjustment paths that will minimize the total cost associated with product failure and adjustment cost. The proposed methodology includes: (i) discretization of space from ud to uf, orthogonally, with defined resolution; (ii) identification of warranty cost at each discretised node based on overlap between manufacturing distribution and in-tolerance NFF regions considering node as production nominal; and (iii) determination of the multi-step function process adjustment between ud and uf using recursive dynamic programming formulation. The methodology is illustrated through numerical simulation.

Polymer Extrusion: From Control System Design to Product Quality

As a major polymer processing technique, polymer extrusion is a continuous process, during which material properties, machine variables, and process variables interact with each other to determine the final product quality. Precise control of key process variables such as barrel temperatures and melt pressure are crucial to ensure a good product quality in the extrusion process. In this paper, the overall extruder control system is constructed by two independent control loops, a single-input–single-output (SISO) control of the melt pressure at die output, and a multiinput–multioutput (MIMO) control of the barrel temperatures. The dynamic behaviors of melt pressure and barrel temperatures were analyzed first. The characteristics of the melt pressure dynamics were as follows: nonlinear and time-varying while the extruder barrel temperatures were nonlinear, slow response, and different zones were highly coupled. Advanced control algorithms were adopted to control these key variables. Experimental results demonstrate the fast response, near-zero overshoot, and precise tracking performance of the proposed control strategies. The robustness of the entire control system was verified through different operating conditions including materials and set points. Ultimately the performance of the entire control system was verified by product quality. The product quality improved significantly with the proposed controller.

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.

Studies on Rheocasting Using Cooling Slope

In the present work, a cooling channel is employed to produce semi-solid A356 alloy slurry. To understand the transport process involved, a 3D non-isothermal, multiphase volume averaging model has been developed for simulation of the semi-solid slurry generation process in the cooling channel. For simulation purpose, the three phases considered are the parent melt, the nearly spherical grains and air as separated but highly coupled interpenetrating continua. The conservation equations of mass, momentum, energy and species have been solved for each phase and the thermal and mechanical interactions (drag force) among the phases have been considered using appropriate model. The superheated liquid alloy is poured at the top of the cooling slope/channel, where specified velocity inlet boundary condition is used in the model, and allowed to flow along gravity through the channel. The melt loses its superheat and becomes semisolid up to the end of cooling channel due to the evolving -Al grains with decreasing temperature. The air phase forms a definable air/liquid melt interface, i.e. free surface, due its low density. The results obtained from the present model includes volume fractions of three different phases considered, grain evolution, grain growth rate, size and distribution of solid grains. The effect of key process variables such as pouring temperature, slope angle of the cooling channel and cooling channel wall temperature on temperature distribution, velocity distribution, grain formation and volume fraction of different phases are also studied. The results obtained from the simulations are validated by microstructure study using SEM and quantitative image analysis of the semi-solid slurry microstructure obtained from the experimental set-up.

A low energy process for the recovery of bioproducts from cyanobacteria using a ball mill

Efficient and economical large scale bioprocessing of microalgae to produce a range of bio-products, working towards a biorefinery approach, is critical for the success of algal industrial biotechnology. The key process variables that affect the recovery of products from different sub-cellular locations were investigated using a high throughput cell disruption system and a cyanobacterium as model organism. This information was then used to design and test a differential recovery procedure at pilot scale using a custom designed ball mill that consumed 1.87kWh/kg of dry biomass used approximately 34% lower than the best algal disruption system reported. The balance between the number of collisions and force of each collision between grinding media and the microorganism were manipulated to achieve differential recovery. Greater than 99 and 95% solids were recovered at the end of first and second ball milling step respectively through gravity sedimentation, an energy efficient solid-liquid separation technique. Based on these results and the release rates of intracellular and/or extracellular products tested, a theoretical framework is presented for the design of a differential recovery process using ball mills.

Interval-Based Diagnosis of Biological Systems - a Powerful Tool for Highly Uncertain Anaerobic Digestion Processes

Anaerobic digestion (AD) is a highly nonlinear time-varying process commonly used for biological wastewater treatment, which is subject to large disturbances of both influent concentrations, and flow rates that may lead the process to a breakdown. In order to compensate the effect of these disturbances, the dynamics of the main state variables – including biomass – must be closely monitored and used to improve the process performance. However, AD processes still suffer from a lack of reliable and cheap sensors of key process variables to insure the right process operation. This has led to the development of estimation schemes, which infer the information of such key variables from the available measurements. Nevertheless, reliable measurements are not always possible to get because these readings may be corrupted by noise or erroneous due to sensor failures and as a consequence, they may lead to deteriorated control efforts and the eventual crash of the AD process. In this article, we propose an integrated system for the detection, isolation, and analysis of faults in AD processes by using interval observers (IO). The proposed approach was experimentally implemented on a 1-m3 pilot scale anaerobic digester. Based on the comparison between the measured outputs and their corresponding estimates, results show that this approach was able to detect sensor failures as well as faults in the basic hypotheses made during the design step.

The importance of key operational variables and electrolyte monitoring to the performance of an all vanadium redox flow battery

AbstractBACKGROUNDThe all vanadium redox flow battery (VRFB) has become the most common type of rfb, however, it is essential to improve our understanding of the importance of key operational variables, including electrode materials, electrolyte flow rate, current density and temperature, on the cell efficiency together with improved methods for cell monitoring.RESULTSSeveral carbon felts were characterized for their electrode activity. A commercially supplied electrolyte of unknown composition was analysed and was shown to contain a VO2+: V3+ ratio of 4.8:1 (total vanadium species = 1.5 mol dm−3) in 4 mol dm−3 H2SO4. A battery (100 cm2) was assembled using three‐dimensional carbon felts and planar carbon feeders as the electrodes with a Nafion® 115 proton exchange membrane. Performance was examined using electrolytes of varying vanadium ion concentration at volumetric flow rating from 0.5–3 mL min1. A suitable volumetric flow rate of electrolyte through each half‐cell was found to be in the range 1.5–2.0 mL min−1, corresponding to a mean linear electrolyte velocity of 1.0–10.1 cm s−1 through the carbon felt electrode. At a constant current charge and discharge current density of 100 mA cm−2 the typical voltage efficiencies were 65%. Charge‐discharge curves were then simulated using a detailed physical model, which generated good quantitative agreement with experimental cell performance.CONCLUSIONSThis study has quantified the effect of key process variables on cell efficiency. A mathematical model has been used successfully to describe charge‐discharge performance and the use of open‐circuit cell voltage has been shown to provide a simple and useful means of cell monitoring. Copyright © 2012 Society of Chemical Industry

Dynamic simulation of MEA absorption process for CO2 capture from power plants

Carbon dioxide (CO2) emissions reduction from combustion fossil fuel power plants is a key aspect in the stabilization of global climate change. Post-combustion CO2 capture from fossil fuel power plants using amine-based solvents, typically monoethanolamine (MEA), is one of the most promising technologies for the abatement of CO2 gas. In this work, a dynamic MEA absorption process model has been developed to predict the dynamic behaviour of the CO2 capture process. The resulting dynamic model was used to study the transient response of the MEA absorption process to changes in the flue gas flow rate and the reboiler heat duty. The changes in flue gas flow rate and reboiler heat duty are key process variables that affect the lean loading, liquid to gas ratio and percentage of CO2 removal. To represent the actual operation of a power plant, the dynamic response of the MEA absorption process to a sinusoidal change in the flue gas flow rate was also considered in the present analysis. The results show that the changes between the reboiler heat duty and CO2 removal follows a ratio of approximately 1:1.4. Also, the lean loading stream entering the absorber unit is key to maintain the feasible operation of the system. Moreover, the energy performance of the process (Joules per mole of CO2 captured) is directly affected by the operation of the reboiler. The outcomes from this study provide insights regarding the dynamic behaviour of this process and can be used as the basis to develop other aspects related to the dynamic operation of this plant, e.g., to develop control strategies for this system.

Fluid Catalytic Cracking Environmental Impact: Factorial Design Coupled with Genetic Algorithms to Minimize Carbon Monoxide Pollution

Fluid Catalytic Cracking (FCC) process is a complex process in petroleum refining industry; it cracks long chain molecules from gas oil and residues to produce high value products like diesel and gasoline. FCC process is composed by two reactors: the riser where cracking reactions take place and the regenerator where combustion reactions eliminate coke deposition from catalyst surface; the last reactors are connected by two transport lines where catalyst circulates. Regenerator flue gas emissions are composed by carbon oxides (CO and CO2), sulfur oxides (SO2 and SO3), nitrogen oxides (NO, N2O, N2), and particulates. This work focuses on the minimization of carbon monoxide (CO) in flue gases while maintaining high process conversion. A multi-objective optimization problem was established to maximize conversion and minimize emissions of CO. The problem was solved using genetic algorithms coupled with factorial design used to identify key process variables and to formulate objective optimization functions. Results showed a reduction in CO emissions in the order of 12.8 % with a conversion of 73 %, indicating genetic algorithms as an useful tool to comply environmental regulations and process demands with low computational burden and time.