Mathematical modelling and control of agitated extraction columns

Liquid-liquid extraction has long been known as a unit operation that plays an important role in industry. This process is well known for its complexity and sensitivity to operation conditions. This thesis presents an attempt to explore the dynamics and control of this process using a systematic approach and state of the art control system design techniques. The process was studied first experimentally under carefully selected. operation conditions, which resembles the ranges employed practically under stable and efficient conditions. Data were collected at steady state conditions using adequate sampling techniques for the dispersed and continuous phases as well as during the transients of the column with the aid of a computer-based online data logging system and online concentration analysis. A stagewise single stage backflow model was improved to mimic the dynamic operation of the column. The developed model accounts for the variation in hydrodynamics, mass transfer, and physical properties throughout the length of the column. End effects were treated by addition of stages at the column entrances. Two parameters were incorporated in the model namely; mass transfer weight factor to correct for the assumption of no mass transfer in the. settling zones at each stage and the backmixing coefficients to handle the axial dispersion phenomena encountered in the course of column operation. The parameters were estimated by minimizing the differences between the experimental and the model predicted concentration profiles at steady state conditions using non-linear optimisation technique. The estimated values were then correlated as functions of operating parameters and were incorporated in·the model equations. The model equations comprise a stiff differential~algebraic system. This system was solved using the GEAR ODE solver. The calculated concentration profiles were compared to those experimentally measured. A very good agreement of the two profiles was achieved within a percent relative error of ±2.S%. The developed rigorous dynamic model of the extraction column was used to derive linear time-invariant reduced-order models that relate the input variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output variables (raffinate concentration and extract concentration) using the asymptotic method of system identification. The reduced-order models were shown to be accurate in capturing the dynamic behaviour of the process with a maximum modelling prediction error of I %. The simplicity and accuracy of the derived reduced-order models allow for control system design and analysis of such complicated processes. The extraction column is a typical multivariable process with agitator speed and solvent feed flowrate considered as manipulative variables; raffinate concentration and extract concentration as controlled variables and the feeds concentration and feed flowrate as disturbance variables. The control system design of the extraction process was tackled as multi-loop decentralised SISO (Single Input Single Output) as well as centralised MIMO (Multi-Input Multi-Output) system using both conventional and model-based control techniques such as IMC (Internal Model Control) and MPC (Model Predictive Control). Control performance of each control scheme was. studied in terms of stability, speed of response, sensitivity to modelling errors (robustness), setpoint tracking capabilities and load rejection. For decentralised control, multiple loops were assigned to pair.each manipulated variable with each controlled variable according to the interaction analysis and other pairing criteria such as relative gain array (RGA), singular value analysis (SVD). Loops namely Rotor speed-Raffinate concentration and Solvent flowrate Extract concentration showed weak interaction. Multivariable MPC has shown more effective performance compared to other conventional techniques since it accounts for loops interaction, time delays, and input-output variables constraints.

BTX extraction by sulfolane in a mixer-settler cascade

The literature relating to the extraction of the aromatics, benzene, toluene and xylene (BTX) using different commercial solvents, and to mixer-settler design and performance, has been reviewed. Liquid-liquid equilibria of the ternary systems: hexane-benzene-sulfolane, n-heptane-toluene-sulfolane, and octane-xylene-sulfolane were determined experimentally at temperatures of 30oC, 35oC, and 40oC. The work was then extended to a multicomponent system. The data were correlated by using Hand's method and were found to be in a good agreement with theoretical predictions using the UNIFAC method. A study was made of the performance of a 10-stage laboratory mixer-settler cascade for the extraction of BTX from a synthetic reformate utilizing sulfolane as a solvent. Murphree stage efficiency decreased with stage number but 99% extraction was achievable within 4 stages. The effects of temperature, phase ratio, and agitator speed were investigated. The efficiency increased with agitator speed but > 1050 rpm resulted in secondary haze formation. An optimum temperature of 30oC was selected from the phase equilibria; the optimum solvent: feed ratio was 3:1 for 4 stages. The experimental overall mass transfer coefficients were compared with those predicted from single drop correlations and were in all cases greater, by a factor of 1.5 to 3, due to the surface renewal associated with drop break-up and coalescence promoted by agitation. A similar investigation was performed using real reformate from the Kuwait Oil Company. The phase ratios were in the range 0.5 to 1 to 3.25 to 1, the agitator speed 1050 rpm, and the operating temperature 30oC. A maximum recovery of 99% aromatics was achieved in 4 stages at a phase ratio of 3.25 to 1. A backflow model was extended to simulate conditions in the mixer-settler cascade with this multicomponent system. Overall mass transfer coefficients were estimated by obtaining the best fit between experimental and predicted concentration profiles. They were up to 10% greater than those with the synthetic feed but close agreement was not possible because the distribution coefficient and phase ratio varied with stage number. Sulfolane was demonstrated to be an excellent solvent for BTX recovery and a mixer-settler cascade was concluded to be a technically viable alternative to agitated columns for this process.

A study of liquid-liquid extraction in a sieve plate column

The literature relating to sieve plate liquid extraction columns and relevant hydrodynamic phenomena have been surveyed. Mass transfer characteristics during drop formation, rise and coalescence, and related models were also reviewed. Important design parameters i.e. flooding, dispersed phase hold-up, drop size distribution, mean drop size, coalescence/flocculation zone height beneath a plate and jetting phenomena were investigated under non-mass transfer and mass transfer conditions in a 0.45m diameter, 2.3m high sieve plate column. This column had provision for four different plate designs, and variable plate spacing and downcomer heights, and the system used was Clairsol `350' (dispersed) - acetone - deionised water (continuous) with either direction of mass transfer. Drop size distributions were best described by the functions proposed by Gal-or, and then Mugele-Evans. Using data from this study and the literature, correlations were developed for dispersed phase hold-up, mean drop size in the preferred jetting regime and in the non-jetting regime, and coalescence zone height. A method to calculate the theoretical overall mass transfer coefficient allowing for the range of drop sizes encountered in the column gave the best fit to experimental data. This applied the drop size distribution diagram to estimate the volume percentage of stagnant, circulating and oscillating drops in the drop population. The overall coefficient Kcal was then calculated as the fractional sum of the predicted individual single drop coefficients and their proportion in the drop population. In a comparison between the experimental and calculated overall mass transfer coefficients for cases in which all the drops were in the oscillating regime (i.e. 6.35mm hole size plate), and for transfer from the dispersed(d) to continuous(c) phase, the film coefficient kd predicted from the Rose-Kintner correlation together with kc from that of Garner-Tayeban gave the best representation. Droplets from the 3.175mm hole size plate, were of a size to be mainly circulating and oscillating; a combination of kd from the Kronig-Brink (circulating) and Rose-Kintner (oscillating) correlations with the respective kc gave the best agreement. The optimum operating conditions for the SPC were identified and a procedure proposed for design from basic single drop data.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

Enhancing enterprise knowledge processes via cross-media extraction

In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.

Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Optimal embedding for watermarking in discrete data spaces

In this paper, we address the problem of robust information embedding in digital data. Such a process is carried out by introducing modifications to the original data that one would like to keep minimal. It assumes that the data, which includes the embedded information, is corrupted before the extraction is carried out. We propose a principled way to tailor an efficient embedding process for given data and noise statistics. © Springer-Verlag Berlin Heidelberg 2005.

Novel evaluation procedure for internal and extraction efficiency of high-power blue LEDs

Internal quantum efficiency (IQE) of a high-brightness blue LED has been evaluated from the external quantum efficiency measured as a function of current at room temperature. Processing the data with a novel evaluation procedure based on the ABC-model, we have determined separately IQE of the LED structure and light extraction efficiency (LEE) of UX:3 chip. Full text Nowadays, understanding of LED efficiency behavior at high currents is quite critical to find ways for further improve­ment of III-nitride LED performance [1]. External quantum ef­ficiency ηe (EQE) provides integral information on the recom­bination and photon emission processes in LEDs. Meanwhile EQE is the product of IQE ηi and LEE ηext at negligible car­rier leakage from the active region. Separate determination of IQE and LEE would be much more helpful, providing correla­tion between these parameters and specific epi-structure and chip design. In this paper, we extend the approach of [2,3] to the whole range of the current/optical power variation, provid­ing an express tool for separate evaluation of IQE and LEE. We studied an InGaN-based LED fabricated by Osram OS. LED structure grown by MOCVD on sapphire substrate was processed as UX:3 chip and mounted into the Golden Dragon package without molding. EQE was measured with Labsphere CDS-600 spectrometer. Plotting EQE versus output power P and finding the power Pm corresponding to EQE maximum ηm enables comparing the measurements with the analytical rela­tionships ηi = Q/(Q+p1/2+p-1/2) ,p = P/Pm , and Q = B/(AC) 1/2 where A, Band C are recombination constants [4]. As a result, maximum IQE value equal to QI(Q+2) can be found from the ratio ηm/ηe plotted as a function of p1/2 +p1-1/2 (see Fig.la) and then LEE calculated as ηext = ηm (Q+2)/Q . Experimental EQE as a function of normalized optical power p is shown in Fig. 1 b along with the analytical approximation based on the ABC­model. The approximation fits perfectly the measurements in the range of the optical power (or operating current) variation by eight orders of magnitude. In conclusion, new express method for separate evaluation of IQE and LEE of III-nitride LEDs is suggested and applied to characterization of a high-brightness blue LED. With this method, we obtained LEE from the free chip surface to the air as 69.8% and IQE as 85.7% at the maximum and 65.2% at the operation current 350 rnA. [I] G. Verzellesi, D. Saguatti, M. Meneghini, F. Bertazzi, M. Goano, G. Meneghesso, and E. Zanoni, "Efficiency droop in InGaN/GaN blue light-emitting diodes: Physical mechanisms and remedies," 1. AppL Phys., vol. 114, no. 7, pp. 071101, Aug., 2013. [2] C. van Opdorp and G. W. 't Hooft, "Method for determining effective non radiative lifetime and leakage losses in double-heterostructure las­ers," 1. AppL Phys., vol. 52, no. 6, pp. 3827-3839, Feb., 1981. [3] M. Meneghini, N. Trivellin, G. Meneghesso, E. Zanoni, U. Zehnder, and B. Hahn, "A combined electro-optical method for the determination of the recombination parameters in InGaN-based light-emitting diodes," 1. AppL Phys., vol. 106, no. II, pp. 114508, Dec., 2009. [4] Qi Dai, Qifeng Shan, ling Wang, S. Chhajed, laehee Cho, E. F. Schubert, M. H. Crawford, D. D. Koleske, Min-Ho Kim, and Yongjo Park, "Carrier recombination mechanisms and efficiency droop in GalnN/GaN light-emitting diodes," App/. Phys. Leu., vol. 97, no. 13, pp. 133507, Sept., 2010. © 2014 IEEE.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.