Optimization and design of energy transport system for solar cooking application

This paper proposes a hybrid solar cooking system where the solar energy is transported to the kitchen. The thermal energy source is used to supplement the Liquefied Petroleum Gas (LPG) that is in common use in kitchens. Solar energy is transferred to the kitchen by means of a circulating fluid. Energy collected from sun is maximized by changing the flow rate dynamically. This paper proposes a concept of maximum power point tracking (MPPT) for the solar thermal collector. The diameter of the pipe is selected to optimize the overall energy transfer. Design and sizing of different components of the system are explained. Concept of MPPT is validated with simulation and experimental results. (C) 2010 Elsevier Ltd. All rights reserved.

Hybrid Solar Cooking

In the existing traditional solar cookers, the cooking is performed near the collector which may be at an inconvenient location for cooking purposes. This paper proposes a hybrid solar cooking system where the solar energy is brought to the kitchen. The energy source is a combination of the solar thermal energy and the Liquefied Petroleum Gas (LPG) that is very common in kitchens. The solar thermal energy is transferred to the kitchen by means of a circulating fluid like oil. The transfer of solar heat is a two fold process wherein the energy from the collector is transferred first to an intermediate energy storage tank and then the energy is subsequently transferred from the tank to the cooking load. There are three parameters that are controlled in order to maximize the energy transfer from the collector to the load viz. the fluid flow rate from collector to tank, fluid flow rate from tank to load and the diameter of the pipes. The entire system is modeled using the bond graph approach. This paper discusses the implementation of such a system.

Harvesting undelimbed Scots pine (Pinus sylvestris L.) from first thinnings for integrated production of kraft pulp and energy

The present study evaluates the feasibility of undelimbed Scots pine (Pinus sylvestris L.) for integrated production of pulp and energy in a kraft pulp mill from the technical, economic and environmental points of view, focusing on the potential of bundle harvesting. The feasibility of tree sections for pulp production was tested by conducting an industrial wood-handling experiment, laboratory cooking and bleaching trials, using conventional small-diameter Scots pine pulpwood as a reference. These trials showed that undelimbed Scots pine sections can be processed in favourable conditions as a blend with conventional small-diameter pulpwood without reducing the pulp quality. However, fibre losses at various phases of the process may increase when using undelimbed material. In the economic evaluation, both pulp production and wood procurement costs were considered, using the relative wood paying capability of a kraft pulp mill as a determinant. The calculations were made for three Scots pine first-thinning stands with the breast-height diameter of the removal (6 12 cm) as the main distinctive factor. The supply chains included in the comparison were based on cut-to-length harvesting, whole-tree harvesting and bundle harvesting (whole-tree bundling). With the current ratio of pulp and energy prices, the wood paying capability declines with an increase in the proportion of the energy fraction of the raw material. The supply system based on the cut-to-length method was the most efficient option, resulting in the highest residual value at stump in most cases. A decline in the pulp price and an increase in the energy price improved the competitiveness of the whole-tree systems. With short truck transportation distances and low pulp prices, however, the harvesting of loose whole trees can result in higher residual value at stump in small-diameter stands. While savings in transportation costs did not compensate for the high cutting and compaction costs by the second prototype of the bundle harvester, an increase in transportation distances improved its competitiveness. Since harvesting undelimbed assortments increases nutrient export from the site, which can affect soil productivity, the whole-tree alternatives included in the present study cannot be recommended on infertile peatlands and mineral soils. The harvesting of loose whole trees or bundled whole trees implies a reduction in protective logging residues and an increase in site traffic or payloads. These factors increase the risk of soil damage, especially on peat soils with poor bearing capacity. Within the wood procurement parameters which were examined, the CO2 emissions of the supply systems varied from 13 27 kg m3. Compaction of whole trees into bundles reduced emissions from transportation by 30 39%, but these reductions were insufficient to compensate for the increased emissions from cutting and compaction.

Modeling and design of a solar thermal system for hybrid cooking application

This paper proposes a hybrid solar cooking system where the solar energy is brought to the kitchen. The energy source is a combination of the solar thermal energy and the Liquefied Petroleum Gas (LPG) that is in common use in kitchens. The solar thermal energy is transferred to the kitchen by means of a circulating fluid. The transfer of solar heat is a twofold process wherein the energy from the collector is transferred first to an intermediate energy storage buffer and the energy is subsequently transferred from the buffer to the cooking load. There are three parameters that are controlled in order to maximize the energy transfer from the collector to the load viz, the fluid flow rate from collector to buffer, fluid flow rate from buffer to load and the diameter of the pipes. This is a complex multi energy domain system comprising energy flow across several domains such as thermal, electrical and hydraulic. The entire system is modeled using the bond graph approach with seamless integration of the power flow in these domains. A method to estimate different parameters of the practical cooking system is also explained. Design and life cycle costing of the system is also discussed. The modeled system is simulated and the results are validated experimentally. (C) 2010 Elsevier Ltd. All rights reserved.

Surface resistance and residual losses of Ag?doped YBa2Cu3O7?? thin films on sapphire

High?quality Ag?doped YBa2Cu3O7?? thin films have been grown by laser ablation on R?plane ?1102? sapphire without any buffer layer. Thin films have been found to be highly c?axis oriented with Tc=90 K, transition width ?T?1 K, and transport Jc=1.2×106 A?cm?2 at 77 K in self?field conditions. The microwave surface resistance of these films measured on patterned microstrip resonators has been found to be 530 ?? at 10 GHz at 77 K which is the lowest reported on unbuffered sapphire. Improved in?plane epitaxy and reduced reaction rate between the substrate and the film caused due to Ag in the film are believed to be responsible for this greatly improved microwave surface resistance. © 1995 American Institute of Physics.

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

Evaluation of mechanical losses in a linear motor pressure wave generator

A moving magnet linear motor compressor or pressure wave generator (PWG) of 2 cc swept volume with dual opposed piston configuration has been developed to operate miniature pulse tube coolers. Prelimnary experiments yielded only a no-load cold end temperature of 180 K. Auxiliary tests and the interpretation of detailed modeling of a PWG suggest that much of the PV power has been lost in the form of blow-by at piston seals due to large and non-optimum clearance seal gap between piston and cylinder. The results of experimental parameters simulated using Sage provide the optimum seal gap value for maximizing the delivered PV power.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Veränderungen sensorischer und instrumenteller Parameter während der Gefrierlagerung von Ostseedorsch (Gadus morhua) bei unterschiedlichen Temperaturen

Changes in sensory and instrumental quality parameter sand in thawing drip, cooking drip and total drip loss of frozen stored Baltic cod fillets (Gadus morhua) at different storage temperatures were investigated. Cod fillets stored at –20 °C and –30 °C exhibited the lowest drip losses and obtained the highest sensory scores. Drip losses were found to be highest in cod fillets stored at –10°C and in double frozen fillets stored at –20 °C. These two experiments also gave the lowest sensory scores. The texture parameters increased during storage parallel with storage time. The waterbinding capacity was lowest at –10 °C and almost constant at –30 °C. There is a good correlation between the sensory scores for “tough” and the instrumental texture measurement for hardness and chewiness.

Assessment of post-harvest losses in Nigeria fishery: the Kainji Lake model

The paper provides a description of a methodology used for quantitative assessment of post harvest losses in the Kainji Lake Fishery (Nigeria). The sample population was made up of 314 fisherfolk, 115 processors, 125 fish buyers and 111 fish sellers. For the determination of handling losses, 24,839 fishes weighing 2,389.31 kg belonging to 43 species were examined of which 10% by number and 9% by weight deteriorated at checking and 4% by number and 3% by weight at landing. Processing losses recorded 22% by number and 16% by weight deteriorated prior to and during smoking with the traditional 'Banda' kiln. During marketing, 16% of fish sold had deteriorated and 6% by weight of fish bought also deteriorated, mainly due to insect infestation during storage. Based on the 1995 yield estimate for Kainji Lake fishery, approximately 1000 tons of fish estimated at 80 million Naira were lost during handling alone. This figure would be much higher if the level of losses during processing and marketing are included. This assessment technique is recommended for use in obtaining quantifiable data on post harvest losses from other water bodies in Nigeria

A preliminary investigation into the post harvest losses of fish in Shiroro Lake area

The findings are presented of a study conducted to assess the post harvest losses in Shiroro Lake, Nigeria. The major objectives were to identify and quantify the types of losses, to provide recommendations that would enhance formulation of policy guidelines for utilization and exploitation of the declining fishery resources of the lake