Fuzzy Modelling and Dynamic Simulation of a Fluidised Bed Granulator

Granulation time in fluidized bed granulators

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Granulation of an insoluble powder in a spouted bed

1. During the rotation granulation process, ZrC-W cermet powders of larger specific surface form, other things being equal, spherical granules of smaller size. 2. As the wetting liquid (dibutyl phthalate) content of the powder is increased from 0.1 to 0.6 wt.%, the mean granule size decreases almost linearly; with further increase in dibutyl content, from 0.6 to 0.9%, the granules continue to increase slightly in size if the granulation time does not exceed 2 h. 3. The granule size increases with increase in granulation time. 4. Variation in powder charge weight in the range 40–200 g has virtually no effect upon the rate of granule growth. 5. The density of sintered granules grows with decrease in granule size. The apparent and tap densities of sintered granules and their flowability are independent of their size (for 300 to 80μ fractions).

Manufacturing pharmaceutical granules: Is the granulation end-point a myth?

Influence of operation mode and wastewater strength on aerobic granulation at pilot scale: Startup period, granular sludge characteristics, and effluent quality

Particle growth mechanisms in fluidised bed granulation—I: The effect of process variables

This paper focuses on investigating the cross-border financial contagion based on a fuzzy dynamical system scenario simulation from a perspective of analyzing the volatility of international capital flows for a panel of 50 countries in emerging markets and advanced economies from 1980 to 2011. Increasing evidence has shown that financial globalization has developed into a complex nonlinear dynamical system made up of economic subsystems with extensive financial connections and linkages. The contagion effects of the spread of bonanzas in the 50 countries are identified and analyzed. The Hodrick–Prescott filter is employed to address the long-term net capital inflow trend. The comovement of financial contagion between the source country of financial turbulence and the volatility-affected country is described as a fuzzy dynamical system in which the driving and response systems are coupled. A fuzzy dynamical system scenario simulation model under a liberal economy is established by employing nonlinear differential equations to describe the contagion mechanism and the international capital flow volatility effects. The model is then extended to a dynamical system model with macroeconomic control. The coupling strength uncertainty is addressed by employing an interval type-2 fuzzy theory method. The properties of the volatility equilibrium point for the two models are discussed, and the volatility contagion principles based on locally asymptotic stability analysis are derived to explain the different volatility transmission patterns. Policy suggestions are given in three situations for providing managerial insights for policymakers and the explorations of response strategies are also presented. The global financial crisis in 2008 is used as an experimental study to demonstrate the validity and effectiveness of the simulation and modeling method.

This paper discusses dynamic modeling and simulation of distributed static compensators (DSTATCOM) in system impact studies involving renewable applications, e.g., wind or solar power plants. Two dynamic simulation models of the DTSTACOM are presented and discussed. Dynamic simulations are presented to show that a reactive compensation system (RCS) assembled with the DSTATCOM as a master controller complies with reactive power, voltage control, and low voltage ride-though requirements for a wind power plant. Benchmark and real system models integrate the mechanically switched capacitors and the wind turbine generator reactive power capability into the total RCS system. The reactive characteristics of the DSTATCOM are also discussed.

In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process.

Dynamic simulation is a powerful tool for chemical process optimizing design, which gives an intuitionistic sight for engineer to understand chemical process and analyzing process design. In this work, a dynamic simulation model for the full flow isopropyl benzene plant is presented. The dynamic model is based on detailed configuration of equipment data from a 300,000 t/a isopropyl benzene plant. It was discovered that the benzene column has the problem of pressure fluctuation. The cause of this fluctuation was analysed and the solutions to solve problem were presented by using dynamic model. Emergency condition, and partial power failure, both were considered for isopropyl benzene column design. The dynamic relieving rate was calculated, and the results compared with that calculated from conventional method. In order to avoiding the relieving rate, high integrated protective system (HIPPS) was applied to the dynamic model. The results shown that (1) The reason of benzene column pressure fluctuation is that the solubility of propane in condensate is sensitive to process parameters. A pressure control scheme with tuned PID parameters was proved in good performance; (2) The relieving rate of isopropyl benzene column calculated from dynamic model decrease 65% compared with that of conventional method; (3) The relieving rate of isopropyl benzene column was cut to zero by using HIPPS.

<div class="htmlview paragraph">The purpose of dynamic modeling and simulation of Advanced Life Support (ALS) systems is to help design them. Static steady state systems analysis provides basic information and is necessary to guide dynamic modeling, but static analysis is not sufficient to design and compare systems. ALS systems must respond to external input variations and internal off-nominal behavior. Buffer sizing, resupply scheduling, failure response, and control system design are aspects of dynamic system design. We develop two dynamic mass flow models and use them in simulations to evaluate systems issues, optimize designs, and make system design trades. One model is of nitrogen leakage in the space station, the other is of a waste processor failure in a regenerative life support system.</div> <div class="htmlview paragraph">Most systems analyses are concerned with optimizing the cost/benefit of a system at its nominal steady-state operating point. ALS analysis must go beyond the static steady state to include dynamic system design. All life support systems exhibit behavior that varies over time. ALS systems must respond to equipment operating cycles, repair schedules, and occasional off-nominal behavior or malfunctions. Biological components, such as bioreactors, composters, and food plant growth chambers, usually have operating cycles or other complex time behavior. Buffer sizes, material stocks, and resupply rates determine dynamic system behavior and directly affect system mass and cost. Dynamic simulation is needed to avoid the extremes of costly over-design of buffers and material reserves or system failure due to insufficient buffers and lack of stored material.</div>

Dynamic modelling and simulation of a heated brine spray system

This study aims to (i) develop predictive models using statistical and fuzzy approaches, and (ii) evaluate their forecasting performance. The data were obtained from www.investing.com for the period 1961 to 2017 and focus on five ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, and Thailand. The statistical models used are Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, while the fuzzy approaches include Chen and Singh fuzzy time series models. The dataset was divided into training and test sets in a 75%-25% proportion. ARIMA models capture trends and autocorrelations in time series data, while Exponential Smoothing uses exponentially weighted averages. Fuzzy models are designed to handle uncertainty and linguistic patterns in data. The results show that Singh’s fuzzy model yields the lowest error for Indonesia, while exponential smoothing and Chen fuzzy time series model demonstrate the same lowest error for Malaysia. For the Philippines, exponential smoothing is most accurate, whereas ARIMA and Singh fuzzy time series achieve the smallest error for Singapore. For Thailand, exponential smoothing and ARIMA perform equally well. However, the robustness of the forecasting model cannot be determined from either statistical or fuzzy methods, highlighting the challenge in determining the most robust model for inflation in the ASEAN region. The 75%-25% data split may also limit the generalizability of the findings. This study contributes a rare cross-country comparison of statistical and fuzzy forecasting methods in the ASEAN context. It highlights the importance of model selection based on country-specific inflation behavior and provides insights for improving forecasting strategies in macroeconomic applications.

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation