Browsing by Author "Mouazen, Abdul M."
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Item Open Access Almost 25 years of chromatographic and spectroscopic analytical method development for petroleum hydrocarbons analysis in soil and sediment: State-of-the-art, progress and trends(Taylor & Francis, 2017-10-11) Douglas, Reward; Nawar, S.; Alamar, M. Carmen; Coulon, Frederic; Mouazen, Abdul M.This review provides a critical insight into the selection of chromatographic and spectroscopic techniques for semi-quantitative and quantitative detection of petroleum hydrocarbons in soil and sediment matrices. Advantages and limitations of both field screening and laboratory-based techniques are discussed and recent advances in chemometrics to extract maximum information from a sample by using the optimal pre-processing and data mining techniques are presented. An integrated analytical framework based on spectroscopic techniques integration and data fusion for the rapid measurement and detection of on-site petroleum hydrocarbons is proposed. Furthermore, factors influencing petroleum hydrocarbons analysis in contaminated samples are discussed and recommendations on how to reduce their influence provided.Item Open Access Co-gasification of oil palm biomass in a pilot scale downdraft gasifier(Elsevier, 2020-07-22) Anyaoha, Kelechi E.; Sakrabani, Ruben; Patchigolla, Kumar; Mouazen, Abdul M.The present study focused on co-gasification of empty fruit bunch (EFB), mesocarp fibre (MF) and palm kernel shell (PKS) in a 75 kWth pilot scale downdraft gasifier for possible synergic reactions between the biomass. A series of experiments was carried out using equal blend of EFB, MF, and PKS (particle sizes of 14 and 6.7 mm) and equal blend of MF and PKS. Advanced infrared multi-gas analyser, and thermal conductivity gas analyser were employed to measure the produced gases. The elemental compositions of the raw biomass, ash and slag generated were determined using Scanning Field Emission Gun Scanning Electron Microscopy with accelerating voltage 20.0 kV and working distance 6 mm and the measurements processed using AztecEnergy V2.2 software. The co-gasification of blend of EFB, MF, and PKS, compared to the blend of MF and PKS led to higher gas yield (4.82 and 3.47 m3/kg_biomass), cold gas efficiency (16.2 and 13.37%), and carbon conversion efficiency (56.3 and 34.18%), respectively. When compared to particle size of 14 mm, the PKS of particle size of 6.7 mm in the EFB/MF/PKS blend increased the lower heating value and the higher heating value of the producer gas by 20% and 20.3%, respectively, and the residue yield was 18.6% less. The overall result has provided evidence on the importance of co-gasification of biomass especially EFB, MF and PKS, which will result in increased utilization of EFB.Item Open Access Critical evaluation of oil palm fresh fruit bunch solid wastes as soil amendments: Prospects and challenges(Elsevier, 2018-05-28) Anyaoha, Kelechi E.; Sakrabani, Ruben; Patchigolla, Kumar; Mouazen, Abdul M.Sustainable land use has been identified as one way of tackling challenges related to climate change, population expansion, food crisis and environmental pollution. Disposal of oil palm fresh fruit bunch (FFB) solid wastes is becoming a challenge with an increased demand and production of palm oil. Whilst this poses a challenge, it could be turned into an opportunity by utilising it as a resource and fully valorise it to meet soil and crop demands. This review presents the potentials of FFB solid wastes, which include empty fruit bunch (EFB), mesocarp fibre (MF), palm kernel shell (PKS), as soil ameliorants. The major findings are the following: 1) pyrolysis, gasification, combustion, and composting are processes that can enhance the value of FFB solid wastes. These processes lead to new products including biochar, ash, and compost, which are valuable resources that can be used for soil improvement. 2) The application of EFB mulch, ash from EFB, MF and PKS, biochar from EFB, and PKS, and compost of EFB, and MF led to improvement in soil physico-chemical properties, and growth and performance of sweet corn, mushroom, oil palm, sweet potato, cauliflower plant, banana, maize, cocoa, cassava, eggplants, and pepper. However, reports show that EFB compost and ash led to decrease in growth and performance of okra. Therefore, the use of appropriate conversion technology for FFB solid wastes as soil ameliorants can significantly improve crop yield and soil properties, reduce environmental pollution, and more importantly increase income of oil mill processors and savings for farmers.Item Open Access Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy(Elsevier, 2015-08-14) Nawar, Said; Buddenbaum, Henning; Hill, Joachim; Kozak, Jacek; Mouazen, Abdul M.The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content.Item Open Access Evaluating management zone maps for variable rate fungicide application and selective harvest(Elsevier, 2018-08-23) Whetton, Rebecca L.; Waine, Toby W.; Mouazen, Abdul M.Currently the majority of crop protection approaches are based on homogeneous rate fungicide application (HRFA) over the entire field area. With the increasing pressures on fungicide applications, associated with increased environmental impact and cost, an alternative approach based on variable rate fungicide application (VRFA) and selective harvest (SH) is needed. This study was undertaken to evaluate the economic viability of adopting VRSA and SH in winter wheat and the environmental benefit in terms of chemical reduction is also discussed. High resolution data of crop canopy properties, yellow rust, fusarium head blight (FHB), soil properties and yield were subjected to k-means cluster analysis to develop management zone (MZ) maps for one field in Bedfordshire, UK. Virtual cost-benefit analysis for VRFA was performed on three fungicide application timings, namely, T1 and T2 focused on yellow rust, and T3 focused on FHB. Cost-benefit analysis was also applied to SH, which assumed different selling prices between healthy and grain downgraded due to mycotoxin infection. Results showed that in this study VRFA allowed for fungicide reductions of 22.24% at T1 and T2 and 25.93% at T3 when compared to HRFA. SH reduced the risk of market rejection due to low quality and high mycotoxin content. Gross profit of combining SH and VRFA was £83.35 per hectare per year, divided into SH £48.04 ha−1, and VRFA £8.8 ha−1 for T1 and T2 and £17.7 ha−1 for T3. Total profit when considering soil and crop scanning costs would be £66.85 ha−1 per year, which is roughly equivalent to €80 or $90 ha−1 per year. This study was restricted to a single field but demonstrates the potential of fungicide reductions and economic viability of this MZ concept.Item Open Access Evaluating oil palm fresh fruit bunch processing in Nigeria(SAGE, 2018-01-30) Anyaoha, Kelechi E.; Sakrabani, Ruben; Patchigolla, Kumar; Mouazen, Abdul M.Three routes of oil palm fresh fruit bunch (FFB) processing in Nigeria namely, industrial, small-scale and traditional were compared by means of determining fruit losses associated with each route. The fruits that are not recovered after each process were hand-picked and quantified in terms of crude palm oil (CPO), palm kernel (PK), mesocarp fibre (MF) and palm kernel shell (PKS). The energy value of empty fruit bunch (EFB), MF and PKS were used to determine the value of energy lost for each route. Additionally, the environmental implications of disposal of EFB were estimated, and socio-economics of the industrial and small-scale routes were related. The analysis showed that 29, 18, 75 and 27 kg of CPO, PK, MF and PKS were lost for every 1000 kg of FFB processed with the industrial route, whereas 5.6, 3.2, 1.4 and 5.1 g were lost with the small-scale route, respectively. Approximately 89 kWh and 31 kWh more energy were lost from MF and PKS with the industrial route than the other two routes, respectively. An equivalent of 6670 tonnes carbon dioxide equivalent of methane and nitrogen oxide was released due to the disposal of 29,000 tonnes of EFB from one palm oil mill. The monetary value of lost CPO per 1000 kg of FFB processed in the industrial route is more than the labour cost of processing 1000 kg of FFB in the small-scale route. The advantages of the industrial route are high throughput in terms of FFB processed per hour and high quality of CPO; however, high fruit loss is associated with it and therefore, the poorly threshed EFB is recommended to be fed into the small-scale route.Item Open Access Hyperspectral measurements of yellow rust and fasarium head blight in cereal crops: Part 2: On-line field measurement(Elsevier, 2018-02-08) Whetton, Rebecca L.; Waine, Toby W.; Mouazen, Abdul M.Yellow rust and fusarium head blight cause significant losses in wheat and barley yields. Mapping the spatial distribution of these two fungal diseases at high sampling resolution is essential for variable rate fungicide application (in case of yellow rust) and selective harvest (in case of fusarium head blight). This study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK. The % coverage was assessed based on two methods, namely, infield visual assessment (IVA) and photo interpretation assessment (PIA) based on 100-point grid overlaid RGB images. The spectral data and disease assessments were subjected to partial least squares regression (PLSR) analyses with leave-one-out cross-validation. Results showed that both diseases can be measured with similar accuracy, and that the performance is better in wheat, as compared to barley. For fusarium, it was found that PIA analysis was more accurate than IVA. The prediction accuracy obtained with PIA was classified as good to moderately accurate, since residual prediction deviation (RPD) values were 2.27 for wheat and 1.56 for barley, and R2 values were 0.82 and 0.61, respectively. Similar results were obtained for yellow rust but with IVA, where model performance was classified as moderately accurate in barley (RPD = 1.67, R2 = 0.72) and good in wheat (RPD = 2.19, R2 = 0.78). It is recommended to adopt the proposed approach to map yellow rust and fusarium head blight in wheat and barley.Item Open Access Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study(Elsevier, 2017-12-11) Whetton, Rebecca L.; Hassall, Kirsty L.; Waine, Toby W.; Mouazen, Abdul M.This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions.Item Open Access Modelling the influence of soil properties on crop yields using a non-linear NFIR model and laboratory data(MDPI, 2021-02-16) Whetton, Rebecca L.; Zhao, Yifan; Nawar, Said; Mouazen, Abdul M.This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model.