No abstract
The unorganised expansion and uncontrolled urbanisation development had led to environmental issues involved with river water pollution that occurred within the Melaka River basin. This study aims to identify pollutant sources using GIS-based pattern recognition techniques of Hotspot analysis in the river basin. The selected study area was Melaka River basin, where independent and dependent variables were land use classification and river water quality data. Both data was obtained from government and private sectors for 2015 and 2029. The result indicated that hotspot analysis with the main contributor of Melaka River pollution were S1 (VA) and S5 (NIA and IA) for both years. The findings could benefit the planners and policy makers in understanding the relationship between land use and water quality within a catchment scale to prevent further deterioration that can cause severe pollution. Land resource management adopting a sustainable development concept is considered as one of the best solutions for longterm management of the resources.
Drilling the deep lithology column using PDC bits in the Obayied field of Egypt's Western Desert has been extremely difficult. The field's lithology column represents an amplification of all of the typical lithology characteristics in the Western Desert. The highly interbedded sandstone, siltstone, and shalealong with the variance of such interbedding across the field-has been a significant challenge for well planners and has adversely affected cost per foot. The application is characterized as predominantly abrasive and impact-intensive in the same section, hence challenging for PDC bit durability. To efficiently drill the 8½-in interval, a fundamental change in PDC bit design is required.Considering these formidable challenges, service providers had to evolve PDC bits to meet the constant demand of improving performance and reducing costs. Focus was concentrated on balancing new technology developments and the willingness to invest on field trials. To accomplish these objectives in the Obayied field, the operator and the service provider identified two main problems-developing an in-depth understanding of rock strength characteristics of each individual formation in the deep rock column and its variance across the field, and developing PDC bits that can survive such a challenging rock column with improved durability and ROP.Recently, a novel conical diamond element (CDE) with extreme impact-and abrasion-resistant characteristics has been developed. The CDE has been incorporated at bit center in a new and innovative PDC design, solving the traditional challenge of the inefficient characteristic of PDC bit central area. In addition, a field-wide rock strength study based on sonic and gamma rays logs provided the transparency required for better planning and risk management to resolve the operational inefficiencies traditionally seen in the Obayied field.The new PDC bits utilizing the CDE technology has been deployed in Obayied and has reduced consumption to just 3-4 bits per section in 2014, whereas that number was 8 -10 bits per section averaged in 2006. The new bit has also reduced the average number of days to drill the section from as low as 6 days to reach TD instead of 20 days. Performance gains were achieved both in ROP and footage totals in the most challenging formations, including Alam Al Buwaib, Upper Safa, and Lower Safa. The authors will discuss the benefits of this industry collaboration that achieved exceptional performance improvement leading to dramatic cost savings in the Obayied field.
This study circumscribes the modelling for concentration of Air Pollutant Index (API) in Selangor, Malaysia. The five monitored environmental pollutant concentrations (O3, CO, NO2, SO2, PM10) for ten years (2006 to 2015) data are used in this study to develop the prediction of API. The selected study area is located in rapid urbanised areas and surrounded by a number of industries, and is highly influenced by congested traffic. The principal component regression (PCR) for the combination of the principal component analysis together with multiple regression analysis, and artificial neural network (ANN), are used to predict the API concentration level. An additional approach using a combination method of PCR and ANN are included into the study to improve the API accuracy of prediction. The resulting prediction models are consistent with the observed value. The prediction techniques of PCR, ANN, and a combination method of R2 values are 0.931, 0.956, and 0.991 respectively. The combination method of PCR and ANN are detected to reduce the root mean square error (RMSE) of API concentration. In conclusion, different techniques were used in the combination method of API prediction which had improved and provided better accuracy rather than being dependent on the single prediction model.
This paper describes measurement methods of surface profiles that improve contact-type displacement sensor outputs by focusing on the contact point between the sphere tip of the sensor and the rough surface. We examined the geometry of a surface profile model and compared measurements using various methods with the measurement using a roughness meter. The spherical tip of the contact type displacement sensor touches the measurement surface and detects the displacement. The sphere tip radius of a typical contact-type displacement sensor ranges from 1–3 mm, causing the roughness curve to be “filtered” by the radius of the sphere. Three methods for estimating the valley portion of the surface profile are evaluated in this study: a) linear approximation of the concave portion of the surface profile, b) function approximation of the concave portion, and c) using the known nose radius of the machining tool. The following sphere tip radii were used to measure actual surface profiles: 0.25 mm, 0.5 mm, 1.0 mm and 1.5 mm. Given the conditions of the experimental model, we found that surface profiles with a roughness that approximates a predictable curve can be measured with a high degree of accuracy.
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