Yassin Osman

Every household uses an average of around 360 litres of water each day. About 21% of a typical gas consumption is attributed to heating the water for showers, baths, and hot water from the tap. An environmentally friendly, low-cost device called the CombiSave valve can be used to manage gas and water consumption and should be fitted to most combination boilers to automatically control the flow of water every time a hot tap is turned on. This allows the boiler to heat the water faster and only return the flow to normal once a usable temperature is reached. An experimental test was conducted in the exemplar modern house of Liverpool John Moores University in order to assess the amount of water, energy, and CO2 reduction for varying temperatures and flow rates. The test was carried out for a duration of 9 hours during the daytime between June and October. Although the test was conducted over relatively warm months when ambient water temperatures were higher compared to winter months, results showed that good savings could be achieved through this product. The best savings for gas consumption and hence CO2 reduction were achieved at high water pressure and low temperature setting (40℃) of 36% compared with the case without combiSave. While water consumption was reduced by 56% at full flow rate and 45℃. Further research is needed encompassing multiple occupied dwellings with different family sizes and testing these in extreme weather conditions to see if similar results would be reflected.

Design/methodology/approach This research has measured, using National Student Survey (NSS) criteria, student experiences on an interdisciplinary project on a civil engineering programme. It benchmarks the quality of learning and student understanding and perceptions of learning. The method is based upon a literature review and questionnaire survey of students.
Purpose In line with business goals of customer satisfaction, higher education institutions of learning consider excellent student experience a priority. Teaching and learning are important aspects of satisfaction that are monitored annually by universities using tools such as the NSS. NSS results are useful for educational planning and informing consumer choices. This research measured undergraduate student experiences on an interdisciplinary project using the NSS framework. Hinged on diversity, the purpose was to investigate whether full time, part time and degree apprenticeship students with varied work experience enhance their learning studying together on an interdisciplinary project.
Findings Results indicate good amounts of peer influence on learning in a simulated interdisciplinary team setting supported by a mix of diverse work experience in students’ background.
Originality/value Sections of the NSS are extended with additional questions to capture the impact that full-time, part-time and degree apprenticeship study modes, closely associated with students’ background of job experience, have on teaching and learning.

Maintenance plays a significant role in asset management of power organisations. Utilisation of right maintenance strategy contributes to high availability and reliability of power plants. Inexperience in implementing modern maintenance programmes and lack of utilising effective maintenance strategies results in bad maintenance practices, low productivity, and a negative impact on financial performance. Thus, effective maintenance strategies become a complex task that deals with multifaceted nature of actions like irregular and uncertain nature. Total Productive Maintenance (TPM) is an ideal approach to support the development and implementation of operation performance improvement. It systematically aims to achieve zero breakdowns, zero defects and zero accidents in order to have high overall equipment effectiveness (OEE). Implementation of TPM programme needs strategic planning. However, there has been almost no research applied in this area within Middle-East power plants. In the power sector of Abu Dhabi, assets management is extremely significant, and it thus requires efficient and effective support of equipment management. The aim of this paper is to investigate technical barriers to successful TPM implementation in the Abu Dhabi power industry. The study has been conducted in the context of a leading power company in the UAE. Questionnaires were distributed with 224 employees, including engineers, maintenance planners, technicians, and operators using a postal questionnaire. The findings of this research identified six key barriers, thus: maintenance policy and methods; maintenance resources, tools and spare parts; computerized maintenance management system (CMMS); housekeeping; participation of autonomous maintenance and maintenance audits. With respect to the understanding of these barriers and obstacles in TPM implementation, the findings can contribute towards improved equipment operations and maintenance in power organisations.

The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the catchment.

n this study, potential impacts of ENSO and Indian Ocean SSTs indices on variability of inflow to the Roseires Dam are investigated. The objective is to investigate the relationship between these climatic indices and the river flow to enable building a forecasting tool to give a lead time prediction of inflow to the dam using lead time information of these indices. Ninety one years of annual flow (1914-2004) were used in the analysis. Correlation results showed that the annual inflow to the dam has negative relation with the ENSO-SST index and SST indices from two regions in the Indian Ocean. Two probabilistic prediction models were developed using the concept of conditional probability to forecast the inflow to the dam. Modelling results showed significant improvements in predictability of inflow when these indices are used as predictors. The models developed could contribute to better water management and operation of the dam reservoir.

The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the catchment.

In this paper impact of climate change on precipitation in the arid environment of Iraq is examined.
LARS-WG weather generator was applied to 5 representative regions to model current and future precipitation under climate change. Seven Global Climate Models (GCMs) have been employed to account for any uncertainty on future projection for three selected periods, 2011-2030, 2046-2065 and 2080-2099. Performance of LARS-WG in each site was first evaluated using the Kolmogorov-Smirnov statistical test for fitting wet/dry days in each site, as well as comparison of the mean and standard deviation between the observed and simulated precipitation. The developed LARS-WG models were found to perform well and skilful in simulating precipitation in the arid regions of Iraq as evidenced by the tests carried and the comparison made. The precipitation models were then used to obtain future projections for precipitation using the IPCC scenario SRES A2. Future precipitation results show that most of the Iraq regions are projected to suffer a reduction in annual mean precipitation, especially by the end of the 21st century, while on a seasonal basis most of the regions are anticipated to be better in autumn and winter.

A flexible framework of multi-model of three statistical downscaling approaches was established in which predictions from these models were used as inputs to Artificial Neural Network (ANN). Traditional ANN, Simple Average Method (SAM), and combining models (SDSM, Multiple Linear Regressions (MLR), Generalized Linear Model (GLM)) were applied to a studied site in North-western England. Model performance criteria of each of the primary and combining models were evaluated. The obtained results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various rainfall characteristics under different circumstances. The combining ANN model showed more adaptability by acquiring better overall performance, while GLM, MLR and showed comparable results and the SDSM reveals relatively less accurate results in modelling most of the rainfall amount. Furthermore traditional ANN has been tested and showed poor performance in reproducing the observed rainfall compared with above methods. The results also show that the superiority of the combining approach model over the single models is promising to be implemented to improve downscaling rainfall at a single site.

n many parts of the world, old sewer systems have been designed without consideration for change in climate, so probabilities and risks of sewer surcharge and flooding are elevated due to increase in extreme rainfall events as a consequence of global warming. The current paper is aiming to assess how the climate change on interannual to multidecadal timescale (2020s; 2050s; 2080s) will affect design standards of wastewater networks due to the presumed increase in rainfall intensity and frequency in the Northwest of England area (selected site). Design storms have been analyzed for future rainfall obtained from the UK Climate Projection version 2009 (UKCP09) weather generator (WG), which was applied to the existing urban drainage system to check the level of service in winter and summer seasons. Two emission scenarios (SRES) have been adopted to simulate the greenhouse gas concentration; high scenario (A1FI) and low scenario (B1). Results indicate that the impact of increase in the design storm of the system in winter led to a potential of increase flood volume from manholes and intern basements at risk of flooding with the worst condition associated with 24 h storm in 2080s. Moreover, when this design storm depth increased by only 15%, the corresponding flood volume increase by 40%, this indicates that the relation between the cause of flooding and its consequences is nonlinear. Summer season has an opposite picture, and flood volume is projected to decrease with the increase in the storm duration causing low risk. Considering climate change in this study caused most of urban drainage models runs to be very slow with some interruption in the simulation due to the inflation in some parameters, so caution should be taken.

Changes in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding.