Modelling of induced aeration in turbine aerators by use of radial basis function neural networks

Gas induction in agitated vessels with turbine impellers can be modelled accurately by means of radial basis function neural nets. The results obtained with the radial basis neural net were significantly better than those obtained by multivariate regression models or standard back propagation neural nets. Moreover, by using the radial basis function neural net model, it was possible to conduct a sensitivity analysis of the variables affecting aeration. Increased medium density showed a strong adverse effect, while variation of the viscosity can cause an increase or a decrease in the rate of aeration, depending on the prevailing process conditions.     

Possibilities and problems with the use of models as a communication tool in water resource management

Politicians and policy-makers, as well as modellers, often nurses an expectation that model derived results is an objective source of information that can be used to support decisions. However, several prerequisites have to be dealt with in order to ensure that models can be used as legitimate and efficient tools in water resource management. Based on empirical material from recent studies on the use of models in stakeholder dialogues, mainly focusing on catchment nutrient transport, two central problems are identified: (a) Models are laden with choices and thus depend on assumptions and priorities of modellers. (b) There are several factors that influence ability and willingness of stakeholders (as information recovers) to criticize or accept results of the modelling exercise. Recognized factors likely to influence stakeholders' acceptance of model derived results include issues at stake, stakeholders' ability to criticize model derived information, and their trust in the institutions that have developed or applied the used models. Identified prerequisites for successful use of models in integrated water resource management include: consideration of user relevance, awareness of and preparedness to handle constraints linked to communication of model-based results, transparency of used models and data and of involved uncertainties, mutual respect between experts and stakeholders and between involved stakeholder groups, a robust institutional network, and sufficient time for dialogues. Development and use of strategies for participatory modelling, based on a continuous dialogue between experts and stakeholders is recommended as a way to facilitate that the prerequisites for a successful use of models in water resource management are fulfilled.     

Forecasting Canadian nuclear power station construction costs

Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequences of the decision as possible. This understanding must be provided by the evaluation of future situations. A key consideration in an evaluation is the financial component. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A brief review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A database is built and indexes are prepared. Based on these indexes, an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of 40 years are generated and applications (such as alternative scenario forecasts and range forecasts) to uncertainty assessment and/or decision-making are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the first for Canadian NGS constructions.     

An illustration of verification and validation in the modelling phase of KBS development

Reliability has become a key factor in KBS development. For this reason, it has been suggested that verification and validation (V&V) should become an integrated part of activities throughout the whole KBS development cycle. In this paper, it will be illustrated how the PROLOGA workbench integrates V&V aspects into its modelling environment, such that these techniques can be of assistance in the process of knowledge acquisition and representation. To this end, verification has to be performed incrementally and can no longer be delayed until after the system has been completed. It will be shown how this objective can be realised through an approach that uses the decision table formalism as a modelling instrument.     

地方时与区时在电离层研究中的差异

本文指出混淆地时与区时两个时间概念在电离层研究中带来的差异的严重性，建议使用地方与区时这两个名词，而抛弃“本地时”和“当地时”等容易引起混淆的名词。     

An institution theory of formal meta-modelling in graphically extended BNF

Meta-modelling plays an important role in model driven software development.In this paper,a graphic extension of BNF (GEBNF) is proposed to define the abstract syntax of graphic modelling languages.Fro...     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

Prediction of venturi scrubber efficiency used in an updraft gasifier cleaning train

The performance of a venturi scrubber in the removal of tar from gas in updraft gasification has been studied. The gasifier has been operated with a husk feed rate of 1.6 × 10^?4 kg/s. The venturi scrubber has been operated at a superficial gas velocity of 56.4 m/s at the throat. A wide variety of scrubbing liquids having surface tensions ranging from 0.026 to 0.072 N/m have been used. The Q_g/Q_l, has been varied in the range of 1000–8000. The tar separation efficiency η has been found to vary from 51 to 98.5%. A mathematical model, assuming steady-state operation, has been developed considering very high pseudosolubility of tar in the scrubbing liquids. The predicted values of η have been compared with experimental results. The model satisfactorily explains the tar removal efficiency of the venturi for Q_g/Q_l values ranging from 4000 to 8000 for all scrubbing liquids. The following correlation has been developed for predicting venturi scrubber efficiency: .