Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Analysis of dynamic models for structural insight and model reduction .2. A multi-stage compressor shutdown case-study

In this second paper, the three structural measures which have been developed are used in the modelling of a three stage centrifugal synthesis gas compressor. The goal of this case study is to determine the essential mathematical structure which must be incorporated into the compressor model to accurately model the shutdown of this system. A simple, accurate and functional model of the system is created via three structural measures. It was found that the model can be correctly reduced into its basic modes and that the order of the differential system can be reduced from 51(st) to 20(th). Of the 31 differential equational 21 reduce to algebraic relations, 8 become constants and 2 can be deleted thereby increasing the algebraic set from 70 to 91 equations. An interpretation is also obtained as to which physical phenomena are dominating the dynamics of the compressor add whether the compressor will enter surge during the shutdown. Comparisons of the reduced model performance against the full model are given, showing the accuracy and applicability of the approach. Copyright (C) 1996 Elsevier Science Ltd

Variance reduction properties of dynamic hedging models - empirical evidence from BRICS countries

This Master’s Thesis analyses the effectiveness of different hedging models on BRICS (Brazil, Russia, India, China, and South Africa) countries. Hedging performance is examined by comparing two different dynamic hedging models to conventional OLS regression based model. The dynamic hedging models being employed are Constant Conditional Correlation (CCC) GARCH(1,1) and Dynamic Conditional Correlation (DCC) GARCH(1,1) with Student’s t-distribution. In order to capture the period of both Great Moderation and the latest financial crisis, the sample period extends from 2003 to 2014. To determine whether dynamic models outperform the conventional one, the reduction of portfolio variance for in-sample data with contemporaneous hedge ratios is first determined and then the holding period of the portfolios is extended to one and two days. In addition, the accuracy of hedge ratio forecasts is examined on the basis of out-of-sample variance reduction. The results are mixed and suggest that dynamic hedging models may not provide enough benefits to justify harder estimation and daily portfolio adjustment. In this sense, the results are consistent with the existing literature.

Leaf transmission reduction using moving jaws for dynamic MLC IMRT

The aim of this work is to investigate to what extent it is possible to use the secondary collimator jaws to reduce the transmitted radiation through the multileaf collimator (MLC) during an intensity modulated radiation therapy (IMRT). A method is developed and introduced where the jaws follow the open window of the MLC dynamically (dJAW method). With the aid of three academic cases (Closed MLC, Sliding-gap, and Chair) and two clinical cases (prostate and head and neck) the feasibility of the dJAW method and the influence of this method on the applied dose distributions are investigated. For this purpose the treatment planning system Eclipse and the Research-Toolbox were used as well as measurements within a solid water phantom were performed. The transmitted radiation through the closed MLC leads to an inhomogeneous dose distribution. In this case, the measured dose within a plane perpendicular to the central axis differs up to 40% (referring to the maximum dose within this plane) for 6 and 15 MV. The calculated dose with Eclipse is clearly more homogeneous. For the Sliding-gap case this difference is still up to 9%. Among other things, these differences depend on the depth of the measurement within the solid water phantom and on the application method. In the Chair case, the dose in regions where no dose is desired is locally reduced by up to 50% using the dJAW method instead of the conventional method. The dose inside the chair-shaped region decreased up to 4% if the same number of monitor units (MU) as for the conventional method was applied. The undesired dose in the volume body minus the planning target volume in the clinical cases prostate and head and neck decreased up to 1.8% and 1.5%, while the number of the applied MU increased up to 3.1% and 2.8%, respectively. The new dJAW method has the potential to enhance the optimization of the conventional IMRT to a further step.

Towards clinical computed ultrasound tomography in echo-mode: Dynamic range artefact reduction

Computed ultrasound tomography in echo-mode (CUTE) allows imaging the speed of sound inside tissue using hand-held pulse-echo ultrasound. This technique is based on measuring the changing local phase of beamformed echoes when changing the transmit beam steering angle. Phantom results have shown a spatial resolution and contrast that could qualify CUTE as a promising novel diagnostic modality in combination with B-mode ultrasound. Unfortunately, the large intensity range of several tens of dB that is encountered in clinical images poses difficulties to echo phase tracking and results in severe artefacts. In this paper we propose a modification to the original technique by which more robust echo tracking can be achieved, and we demonstrate in phantom experiments that dynamic range artefacts are largely eliminated. Dynamic range artefact reduction also allowed for the first time a clinical implementation of CUTE with sufficient contrast to reproducibly distinguish the different speed of sound in different tissue layers of the abdominal wall and the neck.

The emission reduction effect and economic impact of an energy tax vs. a carbon tax in China : a dynamic CGE model analysis

Chinese government commits to reach its peak carbon emissions before 2030, which requires China to implement new policies. Using a CGE model, this study conducts simulation studies on the functions of an energy tax and a carbon tax and analyzes their effects on macro-economic indices. The Chinese economy is affected at an acceptable level by the two taxes. GDP will lose less than 0.8% with a carbon tax of 100, 50, or 10 RMB/ton CO2 or 5% of the delivery price of an energy tax. Thus, the loss of real disposable personal income is smaller. Compared with implementing a single tax, a combined carbon and energy tax induces more emission reductions with relatively smaller economic costs. With these taxes, the domestic competitiveness of energy intensive industries is improved. Additionally, we found that the sooner such taxes are launched, the smaller the economic costs and the more significant the achieved emission reductions.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Strong reduction of V(4+) amount in vanadium oxide/hexadecylamine nanotubes by doping with Co(2+) and Ni(2+) ions: Electron paramagnetic resonance and magnetic studies

In this work we present a complete characterization and magnetic study of vanadium oxide/hexadecylamine nanotubes (VO(x)/Hexa NT's) doped with Co(2)+ and Ni(2+) ions. The morphology of the NT's has been characterized by transmission electron microscopy, while the metallic elements have been quantified by the instrumental neutron activation analysis technique. The static and dynamic magnetic properties were studied by collecting data of magnetization as a function of magnetic field and temperature and by electron paramagnetic resonance. At difference of the majority reports in the literature, we do not observe magnetic dimers in vanadium oxide nanotubes. Also, we observed that the incorporation of metallic ions (Co(2+), S = 3/2 and Ni(2+), S = 1) decreases notably the amount of V(4+) ions in the system, from 14-16% (nondoped case) to 2%-4%, with respect to the total vanadium atoms (fact corroborated by XPS experiments) anyway preserving the tubular nanostructure. The method to decrease the amount of V(4+) in the nanotubes improves considerably their potential technological applications as Li-ion batteries cathodes. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3580252]

Using stochastic dynamic programming to determine optimal fire management for Banksia ornata

1. A model of the population dynamics of Banksia ornata was developed, using stochastic dynamic programming (a state-dependent decision-making tool), to determine optimal fire management strategies that incorporate trade-offs between biodiversity conservation and fuel reduction. 2. The modelled population of B. ornata was described by its age and density, and was exposed to the risk of unplanned fires and stochastic variation in germination success. 3. For a given population in each year, three management strategies were considered: (i) lighting a prescribed fire; (ii) controlling the incidence of unplanned fire; (iii) doing nothing. 4. The optimal management strategy depended on the state of the B. ornata population, with the time since the last fire (age of the population) being the most important variable. Lighting a prescribed fire at an age of less than 30 years was only optimal when the density of seedlings after a fire was low (< 100 plants ha(-1)) or when there were benefits of maintaining a low fuel load by using more frequent fire. 5. Because the cost of management was assumed to be negligible (relative to the value of the persistence of the population), the do-nothing option was never the optimal strategy, although lighting prescribed fires had only marginal benefits when the mean interval between unplanned fires was less than 20-30 years.

Low-dose estrogen therapy does not change postexercise hypotension, sympathetic nerve activity reduction, and vasodilation in healthy postmenopausal women

The aim of this study was to determine whether estrogen therapy enhances postexercise muscle sympathetic nerve activity (MSNA) decrease and vasodilation, resulting in a greater postexercise hypotension. Eighteen postmenopausal women received oral estrogen therapy (ET; n = 9, 1 mg/day) or placebo (n = 9) for 6 mo. They then participated in one 45-min exercise session (cycle ergometer at 50% of oxygen uptake peak) and one 45-min control session (seated rest) in random order. Blood pressure (BP, oscillometry), heart rate (HR), MSNA (microneurography), forearm blood flow (FBF, plethysmography), and forearm vascular resistance (FVR) were measured 60 min later. FVR was calculated. Data were analyzed using a two-way ANOVA. Although postexercise physiological responses were unaltered, HR was significantly lower in the ET group than in the placebo group (59 +/- 2 vs. 71 +/- 2 beats/min, P < 0.01). In both groups, exercise produced significant decreases in systolic BP (145 +/- 3 vs. 154 +/- 3 mmHg, P = 0.01), diastolic BP (71 +/- 3 vs. 75 +/- 2 mmHg, P = 0.04), mean BP (89 +/- 2 vs. 93 +/- 2 mmHg, P = 0.02), MSNA (29 +/- 2 vs. 35 +/- 1 bursts/min, P < 0.01), and FVR (33 +/- 4 vs. 55 +/- 10 units, P = 0.01), whereas it increased FBF (2.7 +/- 0.4 vs. 1.6 +/- 0.2 ml (.) min(-1) (.) 100 ml(-1), P = 0.02) and did not change HR (64 +/- 2 vs. 65 +/- 2 beats/min, P = 0.3). Although ET did not change postexercise BP, HR, MSNA, FBF, or FVR responses, it reduced absolute HR values at baseline and after exercise.

A generalised dynamic model for char particle gasification with structure evolution and peripheral fragmentation

A generalised model for the prediction of single char particle gasification dynamics, accounting for multi-component mass transfer with chemical reaction, heat transfer, as well as structure evolution and peripheral fragmentation is developed in this paper. Maxwell-Stefan analysis is uniquely applied to both micro and macropores within the framework of the dusty-gas model to account for the bidisperse nature of the char, which differs significantly from the conventional models that are based on a single pore type. The peripheral fragmentation and random-pore correlation incorporated into the model enable prediction of structure/reactivity relationships. The occurrence of chemical reaction within the boundary layer reported by Biggs and Agarwal (Chem. Eng. Sci. 52 (1997) 941) has been confirmed through an analysis of CO/CO2 product ratio obtained from model simulations. However, it is also quantitatively observed that the significance of boundary layer reaction reduces notably with the reduction of oxygen concentration in the flue gas, operational pressure and film thickness. Computations have also shown that in the presence of diffusional gradients peripheral fragmentation occurs in the early stages on the surface, after which conversion quickens significantly due to small particle size. Results of the early commencement of peripheral fragmentation at relatively low overall conversion obtained from a large number of simulations agree well with experimental observations reported by Feng and Bhatia (Energy & Fuels 14 (2000) 297). Comprehensive analysis of simulation results is carried out based on well accepted physical principles to rationalise model prediction. (C) 2001 Elsevier Science Ltd. AH rights reserved.

Benchmarking nitrogen removal suspended-carrier biofilm systems using dynamic simulation

We are witnessing an enormous growth in biological nitrogen removal from wastewater. It presents specific challenges beyond traditional COD (carbon) removal. A possibility for optimised process design is the use of biomass-supporting media. In this paper, attached growth processes (AGP) are evaluated using dynamic simulations. The advantages of these systems that were qualitatively described elsewhere, are validated quantitatively based on a simulation benchmark for activated sludge treatment systems. This simulation benchmark is extended with a biofilm model that allows for fast and accurate simulation of the conversion of different substrates in a biofilm. The economic feasibility of this system is evaluated using the data generated with the benchmark simulations. Capital savings due to volume reduction and reduced sludge production are weighed out against increased aeration costs. In this evaluation, effluent quality is integrated as well.

Enhanced Race-to-halt: a leakage-aware energy management approach for dynamic priority systems

With progressing CMOS technology miniaturization, the leakage power consumption starts to dominate the dynamic power consumption. The recent technology trends have equipped the modern embedded processors with the several sleep states and reduced their overhead (energy/time) of the sleep transition. The dynamic voltage frequency scaling (DVFS) potential to save energy is diminishing due to efficient (low overhead) sleep states and increased static (leakage) power consumption. The state-of-the-art research on static power reduction at system level is based on assumptions that cannot easily be integrated into practical systems. We propose a novel enhanced race-to-halt approach (ERTH) to reduce the overall system energy consumption. The exhaustive simulations demonstrate the effectiveness of our approach showing an improvement of up to 8 % over an existing work.

An efficient scalable RNS architecture for large dynamic ranges

This paper proposes an efficient scalable Residue Number System (RNS) architecture supporting moduli sets with an arbitrary number of channels, allowing to achieve larger dynamic range and a higher level of parallelism. The proposed architecture allows the forward and reverse RNS conversion, by reusing the arithmetic channel units. The arithmetic operations supported at the channel level include addition, subtraction, and multiplication with accumulation capability. For the reverse conversion two algorithms are considered, one based on the Chinese Remainder Theorem and the other one on Mixed-Radix-Conversion, leading to implementations optimized for delay and required circuit area. With the proposed architecture a complete and compact RNS platform is achieved. Experimental results suggest gains of 17 % in the delay in the arithmetic operations, with an area reduction of 23 % regarding the RNS state of the art. When compared with a binary system the proposed architecture allows to perform the same computation 20 times faster alongside with only 10 % of the circuit area resources.

Dynamic load management in a smart home to participate in demand response events

In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heating systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.