Bayes理论在河流水质模型参数识别中的应用

参数识别是水环境数学模型建模的重要步骤.在实际模拟过程中,往往难以获得理想的数据进行模型参数识别.充分利用研究者已有的经验,可在一定程度上减少模拟过程的风险.Bayes理论为把研究者的经验或前验信息纳入到水质模拟提供了一个定量手段.采用离散Bayes理论的基本方法,以国内某河段实际监测数据为基础,完成了模型的参数识别过程,并对识别结果进行了分析.验证结果表明,采用Bayes理论获得的参数识别结果能够达到模型验证的要求.     

Modelling of time-varying dispersion for elevated pressurised releases without rainout

Many commonly used atmospheric dispersion models are limited to continuous or instantaneous releases only, and cannot accurately simulate time-varying releases. The current paper discusses a new enhanced dispersion formulation accounting for time-varying effects resulting from a pressure drop in a vessel or pipe, and presuming no rainout. This new formulation is implemented in the Unified Dispersion Model (UDM), and is planned to be included in a future version of Phast.First existing methods are summarised for modelling finite-duration and time-varying releases, and limitations of these methods are identified.Secondly the new mathematical model is summarised. The new formulation presumes a number of ‘observers’ to be released at successive times from the point of discharge. The UDM carries out pseudo steady-state calculations for each observer, where the release data correspond to the time at which the observer is released. Subsequently the model applies a correction to the observer concentrations to ensure mass conservation when observers move with different velocities. Finally effects of along-wind diffusion (due to ambient turbulence) are included by means of Gaussian integration over the downwind distance. This results in reduced concentrations while the cloud travels in the downwind direction.The benefits of the new UDM methodology are illustrated for the case of a H₂S toxic release from a long pipeline representative of some extremely sour fields in the Middle East that are now being developed. Using corrected observer concentrations and along-wind diffusion significantly reduces toxic effect distances when compared to the current Phast 7.1 approach.     

An adapted companion modelling approach for enhancing multi-stakeholder cooperation in complex river basins

The sustainable development agenda 2030 calls for achievement of certain targets to ensure access to water and sanitation for all. Multi-stakeholder partnerships and the use of data and modelling tools are conditioning elements for their achievement. In this article, we demonstrate that participatory modelling supports informed and participatory decision making in complex river basins. An adapted companion modelling approach is presented to support collective action by reducing disputes and enhancing collaboration among stakeholders. The co-development and use of empirical models for understanding the complexity of the physical system is combined with the use of role-playing games to ensure the active involvement of stakeholders. The approach is implemented in a top-down water quality planning process in Turkey. Results show its suitability for managing water quality in complex river basins in an inclusive manner and its substantial benefits in developing stakeholders’ capacities and creating a cooperative environment.     

Organizational adoption of sustainable manufacturing practices in India: integrating institutional theory and corporate environmental responsibility

Organizations are increasingly engaging in socially and environmentally sustainable initiatives. This paper focuses on the institutional isomorphism mechanisms to analyse the organizational adoption of sustainable manufacturing practices like eco-design, source reduction, and environmental management practices. This paper empirically examines the role of institutional influence in the organizational adoption of sustainable manufacturing practices among firms in India. The results of this study confirm the significance of the three institutional isomorphism mechanisms in influencing sustainable responses, but not all mechanisms influence the organizational response equally. In particular, the results show a strong influence of self-regulatory normative isomorphism on the implementation.     

On modelling the global copper mining rates,market supply,copper price and the end of copper reserves

The world supply and turnover of copper was modelled using simple empirical estimates and a COPPER systems dynamics model developed for this study. The model combines mining, trade markets, price mechanisms, population dynamics, use in society and waste as well as recycling, into a whole world system. The degree of sustainability and resource time horizon was estimated using four different methods including (1) burn-off rates, (2) peak discovery early warning, (3) Hubbert's production model, and (4) COPPER, a system dynamics model. The ultimately recoverable reserves (URR) have been estimated using different sources that converge around 2800 million tonne, where about 800 million tonne have already been mined, and 2000 million tonne remain. The different methods independently suggest peak copper mine production in the near future. The model was run for a longer period to cover all systems dynamics and delays. The peak production estimates are in a narrow window in time, from 2031 to 2042, with the best model estimate in 2034, or 21 years from the date of writing. In a longer perspective, taking into account price and recycling, the supply of copper to society is estimated to run out sometime after 2400. The outputs from all models put focus on the importance of copper recycling so that society can become more sustainable with respect to copper supply.     

Smart meters for enhanced water supply network modelling and infrastructure planning

To design water distribution network infrastructure, water utilities formulate daily demand profiles and peaking factors. However, traditional methods of developing such profiles and peaking factors, necessary to carry out water distribution network modelling, are often founded on a number of assumptions on how top-down bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters allows for a new novel methodology for using the continuous ‘big data’ generated by these meter fleets to create evidence-based water demand curves suitable for use in network models. To demonstrate the application of the developed method, high resolution water consumption data from households fitted with smart water meters were collected from the South East Queensland and Hervey Bay regions in Australia. Average day (AD), peak day (PD) and mean day maximum month (MDMM) demand curves, often used in water supply network modelling, were developed from the herein created methodology using both individual end-use level and hourly demand patterns from the smart meters. The resulting modelled water demand patterns for AD, PD and MDMM had morning and evening peaks occurring earlier and lower main peaks (AD: 12%; PD: 20%; MDMM: 33%) than the currently used demand profiles of the regions’ water utility. The paper concludes with a discussion on the implications of widespread smart water metering systems for enhanced water distribution infrastructure planning and management as well as the benefits to customers.