The Government of Malaysia has set a striving target to achieve a higher usage of renewable energy (RE) in the energy mix which is currently around 2% of the country’s electricity. Yet, the government intends to increase this ratio up to 20% by the year 2025. Most of the renewable energy in Malaysia comes from hydropower and biomass sources. Meanwhile, numerous studies have been conducted to determine the feasibility of wind energy in Malaysia. Several locations were reported to be economically viable for wind energy development such as Kudat, Mersing, and Kuala Terengganu. This study presents and discusses the whole life cycle cost analysis of an offshore wind farm in Kudat, Malaysia and determines the cost drivers of offshore wind energy developments. It covers the wind data collection and analysis, breakdown of whole life cycle cost structure, and calculation of the levelized cost of energy (LCOE). Results showed that almost 67% of the total cost was incurred by the capital expenditure (CAPEX), and around 26% by operation and maintenance costs (OPEX), while decommissioning costs (DECOM) reached up to 7% of the whole life cycle costs. The LCOE was calculated and determined to be USD 127.58/MWh.

Slope failure is a common issue in tropical countries. The rise of groundwater table due to rainfall is one of the main triggering factors. There are several methods for slope stabilization such as soil nailing, retaining walls, cut and fill, vegetation and so on. Most of those methods are costly and we are in need for stabilizing methods that are more economical and easier to construct. This article introduces a new method for slope stability. This method is examined numerically and experimentally. It is represented in an automatic zero-energy groundwater withdrawal system to enhance slope stability. The system is validated in a pre-fabricated model to ensure that it works on natural soil slope. The numerical simulation is performed in Soilworks software with coupled seepage-slope stability analysis using finite element methods to check the safety factor with and without the system. The effectiveness of this method is investigated with various rainfall intensities and soil permeabilities. The results for slopes with the application of groundwater withdrawal system are compared with the results without the system. The results demonstrate the effectiveness of the proposed method in reducing groundwater table and enhancing slope stability. The factor of safety for the slope with high soil permeability drops from 1.312 before the rainfall to 1.292 and 0.93 after the third rainfall event for the slope with and without pumping groundwater respectively. For soil slope with moderate soil permeability, the factor of safety deteriorates from 1.314 to 1.157 at the end of the third day, while it remains stable with pumping groundwater. Matric suction is highly increased at the crest of the slope due to pumping.

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One of the necessities of an effective oil and gas pipeline safety Management Plan (SMP) is the establishment of safe and efficient risk assessment strategy for pipelines where the significant danger is corrosion. Corrosion growth is related to several factors involving pipe material, pipe condition, and defect geometrical imperfection. Thus, the assurance of a proper corrosion assessment requires the prediction and evaluation of corrosion growth rates. The prediction of corrosion growth rate precisely, would minimize the cost of pipelines maintenance through the determination of the deteriorated pipeline segments. In line inspection (ILI) has been used to detect the pipelines corrosion, also the corrosion can be detected by other inspection tools such as Magnetic flux leakage (MFL) and Ultrasonic tool (UT). However, there are numerous models have been utilized to anticipate the corrosion growth rate such as deterministic and probabilistic models. Recently, there are conducted researches on the application of artificial intelligence in predicting corrosion growth rate for oil and gas pipelines such as artificial neural network (ANN) and fuzzy logic (FL). This paper aims to provide a comprehensive comparison between the conventional methods, i.e. deterministic and probabilistic and artificial intelligence methods, i.e. Artificial neural network (ANN) and fuzzy logic (FL) in the prediction of corrosion growth rate of oil and gas pipelines. This review would be helpful to pipelines operators to understand the effectiveness of artificial intelligence approach compared to conventional methods in corrosion growth rate modelling.

Landslide is a major issue in tropical countries. The intensive rainfall is the main triggering factor for a landslide that causes a loss in lives and properties. Landslide's triggering factors are several such as rain infiltration, earthquake, and human activities and so on. Those factors are very common. In this paper, the effect of rising of groundwater table in triggering landslide with respect to soil type, soil permeability and rain intensity in a regional scale were studied by running coupled seepage-slope analysis using SOILWORKS software. The results indicate that soil slopes with high permeability coefficient are prone to fail during rainstorm due to the high infiltration of rainwater and the quick rise of the groundwater table, which increases the pore-water pressure. The highest rain infiltration occurs during the first rainfall event and declines at the second and third rainfall due to the saturation of soil at the top layer and the development of a perched water table. It was noticed that the negative pore water pressure increased above the groundwater table and reached its max at the crest of the slope due to the absence of wetting front and the movement of voids with the advancement of the groundwater table. Both high and low rainfall intensities have the same effect on the deep groundwater table. Sandy-silt soil slope was highly affected by rainfall infiltration in comparison with Sandy-clay and Silty-clay slope due to the difference in soil suction where it rose up-60 kpa with Sandy-silt slope after 8 hours of the rainfall which allows more rainwater to infiltrate comparing to other soil slopes which rose up to-21 and-17 kpa for Sandy-clay and Silty-clay slopes respectively. The groundwater table rises above the toe level of the slope causing the factor of safety to drop from 1.312 to 0.93 at the end of the third day. The study indicates that the rainfall at far field of the slope could trigger landslide due to the rise of the groundwater table.