In a mechanical pulping process, (TMP) wood is refined to pulp in a process with very high wood utilization. However, the power demand in the process is high. Thus efficient energy recovery, especially steam recov-ery, is very important. In high consistency (HC) refining the pulp wood is refined at high temperature (140°C) and pressure. The high temperature makes it possible to recover process heat with usable steam properties.
One strategy to decrease the power consumption is to split the refining into two stages, one HC-stage and one low consistency (LC) refining stage. This kind of sys-tem is quite common today. One drawback with LC-refining is that it operates at a low temperature normally below 100°C. Hence, the steam recovery potential from conventional LC-refining is limited.
In this project, we analyse three concepts of steam re-covery in LC-refining by increasing the temperature in the LC-stage. Two base cases: Conventional HC refin-ing only and conventional HC/LC refining is compared with three steam recovery cases: Pulp/Pulp heat ex-changing, Screw Press Dewatering combined with proc-ess water re-circulation and finally Pulp/Water Heat Exchanging.
The study shows that it is possible to recover steam from the LC-stage and, hence, increase the energy effi-ciency of a combined HC/LC refining system. The screw press case has the highest steam recovery poten-tial of the HC/LC configurations. An initial economic estimate indicates that steam recovery in LC-refining is profitable compared to a conventional HC/LC-configuration.
Normally, steam recovery from a conventional low consistency (LC) mechanical pulprefining system is not possible. This is due to the fact that the temperature level in theLC-refiner is less than 100°C. The steam with such a low temperature and associatedpressure has limited value in the mill. In this project, we study a concept of increasingthe temperature in the refiner to a level were process steam with higher quality can berecovered. The temperature level can be increased by transferring heat from outgoingpulp or drainage to incoming pulp or water. This makes it possible to recover heat fromthe process.An initial estimate indicates that steam recovery from LC-refining systems may have agood economic potential. Three cases have been analyzed: Case A: Steam recovery incombination with pulp/pulp heat exchanging, Case B: Steam recovery in combinationwith a pressurized screw press and finally Case C: steam recovery in combination withpump/water heat exchanging.Case B show the best specific steam recovery, 87% kWh recovered steam per kWh usedelectricity. This concept has a lower technological uncertainty compared to cases A andC as it does not need heat exchanging from pulp.The specific heat recovery from case A and C is 78% and 82% respectively. However,the suggested heat exchangers used in these cases do not exist on the market today.There is hence a need for development of exchangers that can handle pulp with highviscosity. The technological risk associated with the screw press scenario is lower and itis likely that this concept is easier to implement.
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The MondiPackaging Dynäs pulp & paper mill in Sweden produces strong sack and kraft paper from chemical softwood pulp. Despite good quality a project was initiated by a need for less variability in paper quality, and less energy consumption. It was known that there is variability in incoming stock, and that the HC and LC refining strategies could be improved. The aim of the project is to reduce energy consumption by 10%, and to reduce quality variation by installing a new online fibre analyzer, PulpEye, and implementing modern modelling and control methodology. We have used multivariate subspace models in combination with optimization to create an OPC based interactive application for on-line operator support and optimization of kraft paper production. The application is now installed and is under evaluation. The specific refiner energy has so far been reduced with at least 25%. The decision support application is indicating even more reduction potential.
In real-time industrial process applications, there is often a need to predict the effects of adjustments before actually effectuated. Multivariate prediction models based on historical data works fine for predictions as long as the new X data have the correlation structure preserved. This paper shows that such can anyway be used for �What if �� decision support in presence of correlated X variables, whenever it is possible to create a help model that predicts the influence of the manipulated X variables on the rest of the X variables.
Immense amounts of data are collected into today's modern process monitoring systems. There are, however, few methods that have the capability to grasp the essentials in these, usually heavily correlated, data. The multivariate statistical techniques, principal components (PC) modelling and modelling by projection to latent structures (PLS) are two methods that have a great potential for process monitoring and forecasting in these situations
We present a method for evaluating the discriminative power of compact feature combinations (blocks) using the distance-based scoring measure, yielding an algorithm for selecting feature blocks that significantly contribute to the outcome variation. To estimate classification performance with subset selection in a high dimensional framework we jointly evaluate both stages of the process: selection of significantly relevant blocks and classification. Classification power and performance properties of the classifier with the proposed subset selection technique has been studied on several simulation models and confirms the benefit of this approach.
Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.
Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.
In chemical kinetics and batch processes K variables are measured on the batches at regular time intervals. This gives a J×K matrix for each batch (J time points times K variables). Consequently, a set of N normal batches gives a three-way matrix of dimension (N×J×K). The case when batches have different length is also discussed. In a typical industrial application of batch modelling, the purpose is to diagnose an evolving batch as normal or not, and to obtain indications of variables that together behave abnormally in batch process upsets. Other applications giving the same form of data include pharmaco-kinetics, clinical and pharmacological trials where patients (or mice) are followed over time, material stability testing and other kinetic investigations. A new approach to the multivariate modelling of three-way kinetic and batch process data is presented. This approach is based on an initial PLS analysis of the ((N×J)×K) unfolded matrix ((batch×time)×variables) with ‘local time' used as a single y-variable. This is followed by a simple statistical analysis of the resulting scores and results in multivariate control charts suitable for monitoring the kinetics of new experiments or batches. ‘Upsets' are effectively diagnosed in these charts, and variables contributing to the upsets are indicated in contribution plots. In addition, the degree of ‘maturity' of the batch can be as predicted vs. observed local time. The analysis of batch data with respect to various questions is discussed with respect to typical objectives, overview and summary, classification, and quantitative modelling. This is illustrated by an industrial example of yeast production.