Experimental evaluation and empirical modelling of palm oil mill effluent steam reforming

Ng, Kim Hoong; Cheng, Yoke Wang; Lee, Zhan Sheng; Khan, Maksudur R.; Lam, Su Shiung; Cheng, Chin Kui

doi:10.1016/j.ijhydene.2018.06.164

Cited by 21 publications

(8 citation statements)

References 28 publications

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“…According to the energy demand forecasting results, there are significant difference in the peak time of the energy consumptions in different scenarios (Ng et al, 2018). The peak values are all in the range of 130-140 Million Metric Tonnes at 2025.…”

Section: Co2 Emission Forecasting Resultsmentioning

confidence: 99%

Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

2020

Global NEST: The International Journal

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<p>The long-term forecasting of the energy demand is an important issue of an area’s sustainable development, especially for mega cities such as Beijing. Beijing is changing its energy supply strategy to depend on energy imports from other provinces due to the city’s long-term low carbon sustainable development plan. Beijing has promised that it will reach the peak value of energy consumption by 2050 and the peak value of the carbon emissions by 2030. To understand whether this can be achieved, this study built an energy demand simulation model using the LEAP with different development scenarios. The results show that, the peak value of Beijing’s energy demand is between 108.25 and 131.74 Mtce during the period of 2044 to 2048, while the peak value of carbon emissions is between 134 and 139.38 million tons in 2025. We also find that adjusting the industry structure and improving the tertiary industry’s energy usage efficiency can be efficient ways to reduce energy consumption. These approaches not only reduce the negative influence of the economic development, but also achieve the energy saving and carbon emission reducing requirements. This study provides an interpretation of the implications for the future energy and climate policies of Beijing.</p>

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Section: Co2 Emission Forecasting Resultsmentioning

confidence: 99%

Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

2020

Global NEST: The International Journal

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“…The texture of soil sample was loamy clay to sandy clay loam, and the clay content was 10%-35%. 15 Samples collected at different sampling points were mixed after milling, air-drying, and sieving by 2-mm sieves. Then, the mixed samples were divided into 16 independent samples and loaded into a soil box of 5-cm diameter.…”

Section: Materials and Methods For Remote Sensing Inversion Of Soilmentioning

confidence: 99%

Detection and analysis of soil water content based on experimental reflectance spectrum data

Fan

2020

Asia-Pacific J Chem Eng

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In order to improve the yield of crops, the method of soil water content detection and analysis based on experimental reflectance spectrum data is adopted. From the four aspects of the farmland soil water detection based on reflection spectrum information, soil water content detection in farmland based on spectral absorption characteristics, remote sensing inversion of soil water content of farmland by reflectance spectra data, and soil water content detection in farmland based on near‐infrared spectroscopy, the soil water content of farmland can be analyzed. The absorption valley near 1,920 nm is the characteristic absorption valley of soil water in the black soil, which can be used to predict the water content of the black soil by the spectral characteristic parameters of the absorption valley; soil water will increase the absorption of radiation energy and reduce the overall reflectance spectra of soil. The quantitative monitoring of soil water content can be achieved by using the remote sensing model of soil water content in this paper. In the range of low soil water content, the reflectance decreases with the increase of soil water content in farmland, whereas in the range of high soil water content, it increases with the increase of soil water content in farmland. The correlation between soil water content and normalized spectral reflectance is positive and negative correlation and mainly negative correlation. The method can detect soil water content in farmland under extreme conditions of different texture, soil water, salinity, and organic matter. It is an effective method for soil water content detection.

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“…In order to further analyze the pollution sources of heavy metals in cultivated soil of this mining area, the principal component analysis of four heavy metals in cultivated soil of this mining area is carried out by factor analysis method (Ng et al 2018). Through KMO and Bartlett sphericity test, the sampling data is suitable for factor analysis.…”

Section: P mentioning

confidence: 99%

Real-Time Detection Method for Heavy Metal Pollution in Soil of Mining Area

2020

Global NEST: The International Journal

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<p>Solid wastes and acid wastewater lead to the enrichment of heavy metals in the soil of mining area. Heavy metal pollution causes the decline of soil quality, ecosystem degradation, crop yield reduction, and even threatens human health. For this reason, the real-time detection method for heavy metal pollution in mining area is studied. Taking a mining area as an example, the data of heavy metal content in mining area soil are collected by PLSR model. Based on the collected data, the real-time detection model of heavy metal pollution in mining area soil based on improved analytic hierarchy process and weighted average method is adopted to real-time detect the heavy metal pollution index in the soil of mining area. The results show that the pollution index of Cu, Zn and Pb in the soil of this mining area belongs to heavy pollution, and the pollution of Cd is relatively small. Among them, the pollution index of Pb is the largest among the four heavy metals, and the pollution is quite serious. The pollution sources of Pb, Zn and Cu in this mining area are the same, and the pollution sources of Cd are different from those of other three heavy metals.</p>

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Experimental evaluation and empirical modelling of palm oil mill effluent steam reforming

Cited by 21 publications

References 28 publications

Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

Detection and analysis of soil water content based on experimental reflectance spectrum data

Real-Time Detection Method for Heavy Metal Pollution in Soil of Mining Area

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