Investigations and model validation of a ground-coupled heat pump for the combination with solar collectors

The operation of ground-coupled heat pumps in combination with solar collectors requires comprising knowledge of the heat pump behavior under non-standard conditions. Especially higher temperatures and varying flow rates in comparison to non-solar systems have to be taken into account. Furthermore the dynamic behavior becomes more important.At ISFH, steady-state and dynamic tests of a typical brine/water heat pump have been carried out in order to analyze its behavior under varying operation conditions. It has been shown, that rising source temperatures do only significantly increase the coefficient of performance (COP), if the source temperature is below 10–20 °C, depending on the temperature lift between source and sink. The flow rate, which has been varied both on the source and the sink side, only showed a minor influence on the exergetic efficiency. Additionally a heat pump model for TRNSYS has been validated under non-standard conditions. The results are assessed by means of TRNSYS simulations.     

浅谈实验室检测方法的验证与确认

检验方法是实验室用于实施检测工作所依据的标准方法和技术规范, 方法及检测步骤的不同直接导致了检验结果的不同。本文从检验方法的选择及原则、方法验证和确认的定义、技术要求、开展方式等方面展开论述二者的区别, 以阐述化学分析实验室如何做好方法的选择、验证和确认, 从而保证检验结果的准确性。     

Automated quantitative model-based fault diagnosistic protocol via Assumption State Differences

This treatment describes the details of a systematic protocol useful for performing optimal automated process fault analysis. This implementation generalizes the underlying Boolean logic version of the Method of Minimal Evidence (MOME) developed previously to a highly comprehensive algorithm for performing model-based fault diagnostics. This generalization allows for a more compact treatment of potential single and multiple fault situations, at all levels of possible diagnostic resolution, with both elegant and efficient uniform sensor validation and proactive fault analysis (SV&PFA) diagnostic rules for diagnosing those situations. This Assumption State Differences (ASD) Protocol version of the MOME algorithm thus automates the diagnostic reasoning necessary to continuously perform optimal process fault analysis so that only the underlying well-formulated models are required to achieve such performance. Using this algorithm consequently directly simplifies the solution of the more complicated problem of automated process fault analysis into the much more tractable, and incrementally solvable, problem of adequately modeling normal process operations.     

Analytical method for fast screening and confirmation of multi-class veterinary drug residues in fish and shrimp by LC-MS/MS

A multi-class, multi-residue analytical method based on LC-MS/MS detection was developed for the screening and confirmation of 28 veterinary drug and metabolite residues in flatfish, shrimp and eel. The chosen veterinary drugs are prohibited or unauthorised compounds in Korea, which were categorised into various chemical classes including nitroimidazoles, benzimidazoles, sulfones, quinolones, macrolides, phenothiazines, pyrethroids and others. To achieve fast and simultaneous extraction of various analytes, a simple and generic liquid extraction procedure using EDTA-ammonium acetate buffer and acetonitrile, without further clean-up steps, was applied to sample preparation. The final extracts were analysed by ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). The method was validated for each compound in each matrix at three different concentrations (5, 10 and 20 ng g^–1) in accordance with Codex guidelines (CAC/GL 71-2009). For most compounds, the recoveries were in the range of 60–110%, and precision, expressed as the relative standard deviation (RSD), was in the range of 5–15%. The detection capabilities (CCβs) were below or equal to 5 ng g^–1, which indicates that the developed method is sufficient to detect illegal fishery products containing the target compounds above the residue limit (10 ng g^–1) of the new regulatory system (Positive List System – PLS).     

Benchmark Experiment on Vanadium with D-T Neutrons and Validation of Evaluated Nuclear Data Libraries by Analysis of the Experiment

A neutronics benchmark experiment on vanadium, which is a low activation fusion reactor material, was conducted by using the D-T neutron source facility of FNS/JAERI. Neutron spectra, dosimetry reaction rates, γ-ray spectra and γ-ray heating rates were measured in a vanadium experimental assembly. Benchmark tests for four evaluated nuclear data files were performed by analyzing the experiment. As a result, the following problems were pointed out in view of accuracy of fusion reactor designs. (1) JENDL-FF and JENDL-3.2: Total cross section should be reexamined especially at ～2keV. (2) ENDF/B-VI: Double differential cross sections for 14 MeV neutrons should be revised because of the isotropic angular distribution for continuum neutron emission. Gamma-ray production cross sections are too small and discrete γ-ray peaks are not represented clearly. (3) EFF-3: Gamma-ray production cross sections are too large.     

Performance evaluation of an ANN–GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor

BACKGROUND: An improved resilient back‐propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non‐linear model based on a 2⁴ full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single‐factor‐at‐a‐time experimental strategy. Improved ANN‐GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back‐propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN‐GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above‐mentioned optimization strategy. © 2012 Society of Chemical Industry     

Diagnostic test cases for verifying surface heat transfer algorithms and boundary conditions in building energy simulation programs

Verification and validation are crucial in developing and implementing models. Although there are standards to test energy simulation software, this article describes an additional set of eight test cases that are a combination of analytical cases and numerical cases for solid conduction heat transfer. These tests focus on diagnosing and verifying conductive heat transfer algorithms and boundary conditions in building envelopes or fabrics. As an example, EnergyPlus versions 5, 6 and 7 are tested using these eight test cases. The test cases were useful for detecting several bugs in the code. The authors recommend these test cases as useful complements to existing verification test cases for building envelopes.     

Parameter prediction for cash flow forecasting models

The paper describes the application of the DHSS formula to 27 completed construction projects comprising four types - steel-framed low rise buildings, new build housing developments, housing refurbishment projects, and multi-house ‘pre-paint’ maintenance contracts. Application of the formula to individual projects indicates that the ‘best’ parameter values offer a ten-fold improvement over the published values based on project size. Similar results occur when using the best parameter values for other two parameter models.

Various approaches are considered in attempting to predict the best parameter values of the models based on known characteristics of the project. A multiple linear regression with project value, duration, and type independent variables is shown not to produce any significant improvement on standard DHSS formula predictions. However, a reduction in the number of independent variables by cross validation produces an approximately 25% improvement on standard DHSS formula forecasts outside the data base. Examination of the models derived from this analysis indicate the type of project to be of major importance.     

Concurrent multi-scale modelling and updating of long-span bridges using a multi-objective optimisation technique

This paper aims at developing an innovative technique of concurrent multi-objective optimisation for updating the multi-scale model of long-span bridges. A multi-scale model is established for the purpose of concurrently analysing the global response of the structure and nonlinear local damages in order to assess structural state and local damage evolution or deteriorating, respectively. A multi-objective optimisation technique is proposed in this work for concurrent multi-scale model updating, in which several key issues including the determination of the objective functions and constraint conditions, the multi-objective optimisation algorithm and how to find the optimal solution from many non-inferior solutions are studied. The proposed concurrent multi-objective optimisation technique is applied to update the initial multi-scale model of Runyang Suspension Bridge (RYSB) near Shanghai, and the updated model is validated by the data from the field tests conducted for obtaining the response in global (dynamic properties) and local levels.     

Long-term monitoring and assessment of a precast continuous viaduct

Precast girders have recently been widely employed in the construction of bridges and viaducts. The new bridge over the Tagus River in Portugal, the Lezíria Bridge, comprehends a 9160 m long south approach viaduct, which was built with precast girders made continuous in situ. Given the relevance of this construction, a long-term monitoring system was implemented and measurements were taken since the start of the construction. The observed parameters were concrete strains and temperatures, deck rotations, joint displacements, accelerations and also environment temperature and relative humidity. The work presents the precast structure, the monitoring system and the appraisal of a statistical procedure for the long-term assessment of the structural behaviour. This procedure is based on prediction models, which establish the normal correlation patterns between environmental and material parameters (such as concrete temperature and shrinkage strains), and the observed structural response in terms of strains, rotations and movements of expansion joints. The calculation of the normal correlation pattern comprehends the minimisation of a square error. By applying the prediction model to the structural response measured in the south approach viaduct of the Lezíria Bridge, it was found that this methodology is a feasible tool for real-time damage detection of bridges.