On the upstream inputs into the MODIS primary productivity products using biometric data from oil palm plantations

This study evaluated the influence of upstream inputs into the Moderate Resolution Imaging Spectroradiometer (MODIS) primary productivity products, termed the MOD17, at tropical oil palm plantations (Elaeis guineensis Jacq.). Evaluation of MOD17 using oil palm plantations as test sites is ideal because the plantations are cultivated on large areas which are comparable with the size of MODIS pixels. It is difficult to find test sites covered by other single species in a whole pixel. The upstream inputs studied included (1) MODIS land cover, (2) the National Centers for Environmental Prediction–Department of Energy (NCEP-DOE) Reanalysis 2 meteorological data set, (3) MODIS leaf area index/fraction of photosynthetically active radiation (LAI/fPAR), and (4) MODIS maximum light-use efficiency (maximum LUE). Oil palm biometric and local meteorological data were utilized as ground data. Furthermore, scaling up oil palm LAI and fPAR from plot scale to regional scale (Peninsular Malaysia) was done empirically by correlating oil palm LAI derived from the hemispherical photography technique with radiance information from the Disaster Monitoring Constellation 2 satellite (UK-DMC 2). The upscaled LAI/fPAR developed in this study was used to evaluate the MODIS LAI/fPAR. The results showed that the MODIS land-cover product has an overall accuracy of 78.8% when compared to the Peninsular Malaysia land-use map produced by the Department of Agriculture, Malaysia. Regarding the NCEP-DOE Reanalysis 2 data set, vapour pressure deficit (VPD) and photosynthetically active radiation (PAR) contain large uncertainties in our study area. However, MODIS LAI and fPAR were correlated relatively well with the upscaled LAI (R² = 0.50) and the upscaled fPAR (R² = 0.60), respectively. The constant values of maximum LUE for croplands and evergreen broadleaf forest ecosystems are lower than the maximum LUE of oil palm. The relative predictive error assessment showed that the MOD17 net primary productivity (NPP) overestimated oil palm NPP derived from biometric methods by 142–204%. We replaced the upstream inputs of MOD17 by the local inputs for estimating oil palm GPP and NPP in Peninsular Malaysia. This was done by (1) assigning maximum LUE for oil palm plantations as a constant at 1.68 g C m^?2 day^?1, (2) utilizing meteorological data from local meteorological stations, and (3) using the upscaled fPAR of oil palm plantations. The amount of oil palm GPP and NPP for Peninsular Malaysia in 2010 were estimated to be ~0.09 Pg C year^?1 (or equivalent to ~0.33 Pg CO₂ year^?1) and ~0.03 Pg C year^?1 (~0.11 Pg CO₂ year^?1), respectively, indicating that oil palm plantations in Peninsular Malaysia can play an important role in global carbon sequestration. In the future there is likely to be a demand for MODIS GPP and NPP products that are more accurate than those currently generated by MOD17. We recommend future developments of the MOD17 processing system to allow improvements in the upstream input parameters, in the manner described in this article, both for global processing and for the production of more accurate values for GPP and NPP at regional and local scales.     

A new approach to formulate fuzzy regression models

A fuzzy regression model is developed to construct the relationship between the response and explanatory variables in fuzzy environments. To enhance explanatory power and take into account the uncertainty of the formulated model and parameters, a new operator, called the fuzzy product core (FPC), is proposed for the formulation processes to establish fuzzy regression models with fuzzy parameters using fuzzy observations that include fuzzy response and explanatory variables. In addition, the sign of parameters can be determined in the model-building processes. Compared to existing approaches, the proposed approach reduces the amount of unnecessary or unimportant information arising from fuzzy observations and determines the sign of parameters in the models to increase model performance. This improves the weakness of the relevant approaches in which the parameters in the models are fuzzy and must be predetermined in the formulation processes. The proposed approach outperforms existing models in terms of distance, mean similarity, and credibility measures, even when crisp explanatory variables are used.     

Inferential estimation of kerosene dry point in refineries with varying crudes

A bootstrap aggregated model approach to the estimation of product quality in refineries with varying crudes is proposed in this paper. The varying crudes cause the relationship between process variables and product quality variables to change, which makes product quality estimation by soft-sensors a difficult problem. The essential idea in this paper is to build an inferential estimation model for each type of feed oil and use an on-line feed oil classifier to determine the feed oil type. Bootstrap aggregated neural networks are used in developing the on-line feed oil classifier and a bootstrap aggregated partial least square regression model is developed for each data group corresponding to each type of feed crude oil. The amount of training data in crude oil distillation is usually small and this brings difficulties for classification and estimation modelling. In order to enhance model reliability and robustness, bootstrap aggregated models are developed. The inferential estimation results of kerosene dry point on both simulated data and industrial data show that the proposed method can significantly improve the overall inferential estimation performance.     

CMAS 3D,a new program to visualize and project major elements compositions in the CMAS system

CMAS 3D, developed in MATLAB^®, is a program to support visualization of major element chemical data in three dimensions. Such projections are used to discuss correlations, metamorphic reactions and the chemical evolution of rocks, melts or minerals. It can also project data into 2D plots. The CMAS 3D interface makes it easy to use, and does not require any knowledge of Matlab^® programming. CMAS 3D uses data compiled in a Microsoft Excel? spreadsheet. Although useful for scientific research, the program is also a powerful tool for teaching.     

Application of fuzzy logic for predicting roof fall rate in coal mines

Roof fall is one of the serious hazards associated with underground coal mining. Roof fall can cause fatal and non-fatal injuries on miners, stoppages in mining operations and equipment breakdowns. Therefore, accurate prediction of roof fall rate is very important in controlling and eliminating of related problems. In this study, the fuzzy logic was applied to predict roof fall rate in coal mines. The predictive fuzzy model was implemented on fuzzy logic toolbox of MATLAB^® using Mamdani algorithm and was developed based on experts’ knowledge and also a database including 109 datasets of roof performance from US coal mines. 22 datasets of this database were used to assess the performance of this fuzzy model. The comparison between obtained results from model and actual roof fall rate showed that the fuzzy model can predict roof fall rate very well.

     

Comparing classical and neural regression techniques in modeling crude oil viscosity

The importance of crude oil viscosity makes its accurate determination necessary for reservoir performance calculations, evaluation of hydrocarbon reserves, planning thermal methods of enhanced oil recovery, and designing production equipment and pipelines. Viscosity data are also involved in several dimensionless parameters to calculate flow regimes, friction factors and pressure gradients in multiphase flow problems. Numerous research efforts have been directed towards the development of viscosity models that are capable of accurately predicting crude oil viscosity as a function of production data, and/or composition of well stream fluids, if available, using equation of State. Since fluid compositions are not always available, most of the efforts were focused on developing viscosity correlations using classical regression techniques.The study presents, for the first time, a comparison among several models developed using both classical regression techniques (CRT) and neural regression techniques (NRT). These models are developed in this study from viscosity data collected from different oil fields. The models have also been tested using another collection of viscosity data that was not used before in the development phase. Results show that viscosity models developed using NRT were more accurate than viscosity models developed using CRT. Based on this comparison, a viscosity model is therefore presented, which uses stock-tank oil API gravity, gas gravity, pressure(s), and temperature(s) to predict crude oil viscosity. The model was developed using General Regression Neural Network algorithm.     

常压蒸馏产品质量软测量改进方法及应用

针对石油化工生产过程基于统计模型的产品质量软测量中普遍存在的软测量模型适用工作范围小、难以反映进料原料性质变化的问题，探讨和应用了机理分析和统计建模相结合的软测量方法。针对一个实际常减压蒸馏产品质量软测量和控制问题．通过机理分析、实际操作数据分析并结合操作人员经验，选择能够反映进料原料性质变化的过程变量作为统计模型的输入以克服进料原料性质变化的影响，将有些直接测量的输入变量按照机理关系进行计算得到的新的变量作为统计建模的输入变量，使其和产品质量之间具有更宽范围的近似线性关系，提高软测量模型的泛化能力：某厂常减压蒸馏装置的实际应用结果表明了该方法的有效性。     

Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data

This study developed biomass models to calculate carbon stock levels of the West African oil palms (Elaeis guineensis) using multi-date wet and dry season IKONOS images. Two benchmark areas of the derived savanna eco-regions of Africa were selected for analysis. Allometric equations related above-ground palm biomass to their stem heights. Empirical regression models based on field plot data were established to determine wet and dry biomass (kg m^?2) of oil palm plantations in IKONOS images. The best models were exponential, involving bands 3, 3 and 1, or 3 and 4, and explaining between 63 and 72% of the variability in the data. Model evaluations with independent datasets showed there is 28-36% uncertainty in dry biomass predictions. At the landscape level, multi-date IKONOS data mapped oil palm plantations with an overall accuracy of 88-92%. However, the ability of IKONOS data to differentiate various age groups of oil palms was limited with a high degree of intermixing of classes. The best results were obtained when delineating agro-palm (palms mixed with agriculture and fallows), palm of 1-3 years, and palm of 4-5 years at an overall accuracy of 74.5% using all four IKONOS bands. The results indicate the need for additional spectral bands in the IKONOS sensor. The total carbon per unit area of oil palms was calculated across age groups for the two benchmark areas of West Africa and were 14.75 and 14.94 tonnes ha^?1 (or Mg ha^?1), respectively. The corresponding dry biomass (kg m^?2) were 29.5 and 29.88 tonnes ha^?1 (or Mg ha^?1). The age of the oil palms were between 1 and 5 years across benchmark areas. The mean rate of accumulation of carbon was 2.95 t C ha^?1 year^?1 in benchmark area 1 and 2.99 t C ha^?1 year^?1 in benchmark area 2.     

Modelling and mapping of above ground biomass (AGB) of oil palm plantations in Malaysia using remotely-sensed data

Estimating accurate above ground biomass (AGB) of oil palm plantation in Malaysia is crucial as it serves as an important indicator to assess the role of oil palm plantations in the global carbon cycle, particularly whether it serves as carbon source or sink. Research on oil palm AGB in Malaysia using remote sensing is almost insignificant and it has known that remote sensing provides easy, inexpensive and less time consuming over larger areas. Therefore, this study focuses on evaluating the potential of Landsat Thematic Mapper (TM) data with combination of field data survey to predict AGB estimates and mapping the oil palm plantations. The relationships of AGB with individual TM bands and various selected vegetation indices were examined. In addition, various possibilities of data transform were explored in statistical analysis. The potential models selected were obtained using backward elimination method where R², adjusted R² (R²_adj), standard error of estimate (SE_E), root mean squared error (RMSE) and Mallows’s C_p criterion were examined in model development and validation. It was found that the most promising model provides moderately good prediction of about 62% of the variability of the AGB with RMSE value of 3.68 tonnes (t) ha^-1. In conclusion, Landsat TM offers the low cost AGB estimates and mapping of oil palm plantations with moderate accuracy in Malaysia.     

Development of an oil palm cropping systems model: Lessons learned and future directions

Oil palm has become one of the most important crops in the world with questions being raised about its economic and environmental sustainability. Agricultural systems models are regularly employed in studying sustainable crop management but no detailed model is currently available for oil palm systems.We developed a production systems model for oil palm within the Agricultural Production Systems Simulator (APSIM) framework and tested it using data across a range of environments within Papua New Guinea (PNG). The model captured key growth responses to climate and management. This demonstrates that modern modelling frameworks do allow for rapid model development for new agricultural systems.However, whilst application of the model is promising, the availability of key data is likely to restrict its use. Local soil and weather data are not available in adequate detail for many of the major oil palm production areas, although some methods exist to address this.     

A fuzzy clustering method for generating fuzzy models

A systematic fuzzy approach considering both accuracy and interpretability is developed in the paper. First, a fuzzy modeling method based on a new objective function is proposed. The proposed method can deal with the problem where the input variables have an affect on the input space of the fuzzy system while the output variables do not exert any influence on input space of fuzzy system. Then rule reduction is performed to obtain the model structure of the fuzzy system by QR decomposition of the fuzzy reference matrix. According to analysis of the rank loss of the matrix, the important rules and unimportant rules can be confirmed in this paper. Simulation results demonstrate that the proposed approach can be used to build fuzzy models of nonlinear systems. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Assessing airborne transmission of foot and mouth disease using fuzzy logic

In case of an outbreak of foot and mouth disease, the prediction of airborne spread is an important tool for decision-makers to assess the potential risk of secondary infections. Modelling approaches such as the Gaussian dispersion or Lagrangian particle model have been established but are complex to use and the structure of the models is fixed rather than adjustable to emerging disease situations. The aim of the present study was to evaluate the application of fuzzy logic as a modelling technique based on linguistic variables. Fuzzy logic models are easy to use and to modify. Adaptations to emerging outbreaks seem feasible. Using the Gaussian dispersion model as a reference, livestock-specific fuzzy logic models were developed. In a stepwise modelling process, the input parameters of the Gaussian model were added one-by-one into the fuzzy models. On the basis of weather data and randomly allocated farms, a validation dataset with 10,000 observations was generated and used in a 10-fold cross validation to compare the two modelling approaches. A good agreement between the Gaussian dispersion and the fuzzy logic models concerning the main directions of virus spread were found. The measure of agreement ranged between 87.0% and 99.9%. Falsely classified observations occurred mostly in proximity to the boundary of virus transmission based on the Gaussian dispersion model. In conclusion, fuzzy logic determined the same risk of infection for secondary cases than the Gaussian dispersion model. Limitations to certain livestock were not observed. The inclusion of up to four input variables did not influence the results in a mentionable amount. Including additional input variables into the fuzzy models could improve its application in assessing the risk of airborne foot and mouth disease transmission furthermore.     

Fuzzy inventory models for items with imperfect quality and shortage backordering under crisp and fuzzy decision variables

This paper considers inventory models for items with imperfect quality and shortage backordering in fuzzy environments by employing two types of fuzzy numbers, which are trapezoidal and triangular. Two fuzzy models are developed. In the first model the input parameters are fuzzified, while the decision variables are treated as crisp variables. In the second model, not only the input parameters but also the decision variables are fuzzified. For each fuzzy model, a method of defuzzification, namely the graded mean integration method, is employed to find the estimate of the profit function in the fuzzy sense, and then the optimal policy for the each model is determined. The optimal policy for the second model is determined by using the Kuhn–Tucker conditions after the defuzzification of the profit function. Numerical examples are provided in order to ascertain the sensitiveness in the decision variables with respect to fuzziness in the components.     

Implementation of a Lattice Boltzmann kernel using the Compute Unified Device Architecture developed by nVIDIA

In this article a very efficient implementation of a 2D-Lattice Boltzmann kernel using the Compute Unified Device Architecture (CUDA?) interface developed by nVIDIA^® is presented. By exploiting the explicit parallelism exposed in the graphics hardware we obtain more than one order in performance gain compared to standard CPUs. A non-trivial example, the flow through a generic porous medium, shows the performance of the implementation.     

Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia

This article demonstrates some techniques for studying the age of oil palm trees (Elaeis guineensis Jacq.) using the Disaster Monitoring Constellation 2 from the UK (UK-DMC 2) and Advanced Land Observing Satellite phased array L-band synthetic aperture radar (ALOS PALSAR) remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely (1) radiance, vegetation indices, and fraction of shadow; (2) texture measurement; (3) classifications, namely Iterative Self-Organizing Data Analysis Technique (ISODATA) classification, maximum-likelihood classification (MLC), and random forest (RF) classification; (4) in terms of ALOS PALSAR data, the correlation of polarizations (i.e. horizontal transmitting and horizontal receiving (termed HH polarization) and horizontal transmitting and vertical receiving (termed HV polarization)) and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 (near-infrared) of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix (GLCM) demonstrated strong correlation of determination (R ²?=?0.76–0.80) with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly (R ²?=?0.49) with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% (MLC method) to 52.9%. This study concluded that texture measurement (GLCM mean) and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.     

Automated verification of code automatically generated from Simulink®

The CLawZ toolset independently and automatically proves the correctness of code automatically generated by a commercial auto-code generator for the Simulink^® modelling language. The use of formal methods is invisible to the user and it has been shown to lead to faster development of correct code. The CLawZ toolset has been continually developed and used for over a decade to prove the correctness of embedded real time safety critical software for Eurofighter Typhoon. The only requirement on the commercial auto-coder is that it provides traceability information between the signal wires in a Simulink^® model and the program variables that implement them.     

Integration of reclamation and tailings management in oil sands surface mine planning

The processing of oil sands generates large volumes of slurry, known as tailings, that is impounded in tailings ponds. Oil sands operators are committed to develop reclamation plans to ensure that the mine site is restored to a natural or economically usable landscape. Since most of the material that is needed for capping of the tailings pond is produced in mining operation, it is reasonable to include material requirement for reclamation as part of mine planning. In this paper, an integrated long-term mine planning model is proposed that includes tailings capacity and reclamation material requirements. A mixed integer linear programming (MILP) model is developed to test the performance of the proposed model. The MILP model is coded in Matlab^®. It is verified by carrying out a case study on an actual oil sands dataset, and has resulted in an integer solution within a 2% gap to the optimality. The resulted production schedule meets the capacity constraint of the tailings facility and guarantees the production of the required reclamation material.     

Linear regression modelling for the estimation of oil palm age from Landsat TM

This paper investigates the accuracy with which the age since field planting of oil palm (Elaeis guineensis Jacq.) can be estimated from Landsat Thematic Mapper (TM) radiance at pixel and stand scales. The study site, a commercial plantation 30 km south-east of Kuala Lumpur in Selangor, Malaysia, consisted of even-aged blocks from 4 to 21 years old. Spectral data were the six reflective TM bands and three spectral indices. Nonlinear negative relationships between spectral variables and age are compared to published trends in leaf area, stem height and per cent canopy cover for oil palm and other tree plantations. Correlation coefficients between log age and log radiance are moderate and highly significant (p<0.01) for bands 2-5 and 7 (-0.214 to-0.776) at the pixel scale, and increase at the stand scale (r ²=0.985 for log band 5, p<0.01). Relationships are strongest for the mid-infrared bands, especially band 5 (r ²=0.585, p <0.01) and the infrared index (IRI), a normalized difference index of bands 4 and 5 (r ²= 0.48, p<0.01). Direct and inverse linear regression models for log age with log band and log age with IRI squared (IRIsq) were constructed at both scales. Equivalent age was estimated from the models using independent test sets for differing scales and degrees of aggregation of the age classes. Single age classes cannot be estimated accurately at the pixel or stand scales; the lowest RMS error was obtained from the direct model using all bands (RMS error=3.9 years at pixel scale, 2.7 at stand scale). A posteriori aggregation into generalized age classes (<5, 6-10, 11-15, 16-21 years) improved the RMS error but the results were still unacceptably high (2.2, 2.3, 2.7, 6.0 years respectively for direct model 3 using all bands). Acceptable RMS errors down to 0.58 years were obtained for models using IRIsq with generalized age classes developed and applied at the stand scale when variations in ground cover and other variables were averaged out. The spatial pattern of error in equivalent age deserves investigation for precision crop management.     

Fuzzy logic-based prediction of compressive strength of lightweight geopolymers

In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.