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Fouling of industrial surfaces by silica and calcium oxalate can be detrimental to a number of process streams. Solution chemistry plays a large roll in the rate and type of scale formed on industrial surfaces. This study is on the kinetics and thermodynamics of SiO₂ and calcium oxalate composite formation in solutions containing Mg²+ ions, 'trans'-aconitic acid and sucrose, to mimic factory sugar cane juices. The induction time (ti) of silicic acid polymerization is found to be dependent on the sucrose concentration and SiO₂ supersaturation ratio (SS). Generalized kinetic and solubility models are developed for SiO₂ and calcium oxalate in binary systems using response surface methodology. The role of sucrose, Mg, 'trans'-aconitic acid, a mixture of Mg and 'trans'-aconitic acid, SiO₂ SS ratio and Ca in the formation of composites is explained using the solution properties of these species including their ability to form complexes.

AN ANALYSIS OF time series Landsat imagery acquired over the Bundaberg region between 2010 and 2015 identified variations in annual crop vigour trends, as determined by greenness normalised difference vegetation index (GNDVI). On average, early to mid-April was identified as the crucial period where crops achieved their maximum vigour and as such indicated when single image captures should be acquired for future regional yield forecasting. Additionally, the regional crop GNDVI averaged from Landsat images between February to April, produced a higher coefficient of determination to final yield (R2 = 0.91) than the average crop GNDVI value from a single mid-season SPOT5 image capture (R2 = 0.52). This result indicates that the time series method may be more appropriate for future regional yield forecasting. For improved prediction accuracies at the individual crop level, a univariate model using only crop GNDVI values (SPOT5) and corresponding yield (t/ha) produced a higher prediction accuracy for the 2014 Bundaberg harvest than a multivariate model that included additional historic spectral and crop attribute data. For Condong, a multivariate model improved the prediction accuracy of individual crops harvested in 2014 by 41.8% for one-year-old cane (Y1), and 46.2% for two-year-old cane (Y2). For the non-invasive measure of foliar nitrogen (N%), the specific wavelengths 615 nm, 737 nm and 933 nm (Airborne hyperspectral), and 634 nm, 750 nm and 880 nm (ground based field spectroscopy) were found to be the most significant. These results were supported by satellite imagery (Worldview-2 and Worldview-3) acquired over three replicated field trials in Mackay (2014 and 2015) and Tully (2015), where the vegetation index (VI) REN2NDVIWV, a ratio of the rededge band (705-745 nm) and the Near-IR2 band (860-1040 nm), produced a higher correlation to nitrogen concentration (%) than NDVI.

Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons' harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named 'bins'. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each 'bin' was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha^-1) achieved for the respective bin over the five growing years, producing strong correlations (R² = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha^-1) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R² = 0.87 and RMSE = 11.33 (t·ha^-1)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.

An intrinsic exposed core optical fiber sensors has been described that is capable of monitoring surface crystallization, also known as scale formation. The optical fiber sensor is a more reliable sensor of scale growth than other scale sensing methods, such as turbidity measurement, due to its ability to discriminate between bulk and surface crystallization. When the sensor was subjected to the same crystal growth conditions as a stainless steel surface, the optical fiber sensor showed the capability to follow the scale formation on the scale affected stainless steel surface.

Solution chemistry plays a significant role in the rate and type of foulant formed on heated industrial surfaces. This paper describes the effect of sucrose, silica (Si0₂), Ca²⁺ and Mg²⁺ ions, and trans-aconitic acid on the kinetics and solubility of SiO₂ and calcium oxalate monohydrate (COM) in mixed salt solutions containing sucrose and refines models previously proposed. The developed SiO₂ models show that sucrose and SiO₂ concentrations are the main parameters that determine apparent order ('n') and apparent rate of reaction ('k') and Si02 solubility over a 24 h period. The calcium oxalate solubility model shows that while increasing [Mg²⁺] increases COM solubility, the reverse is so with increasing sucrose concentrations. The role of solution species on COM crystal habit is discussed and the appearance of the uncommon (001) face is explained.

Accurate with-in season yield prediction is important for the Australian sugarcane industry as it supports crop management and decision making processes, including those associated with harvest scheduling, storage, milling, and forward selling. In a recent study, a quadratic model was developed from multi-temporal Landsat imagery (30 m spatial resolution) acquired between 2001-2014 (15th November to 31st July) for the prediction of sugarcane yield grown in the Bundaberg region of Queensland, Australia. The resultant high accuracy of prediction achieved from the Bundaberg model for the 2015 and 2016 seasons inspired the development of similar models for the Tully and Mackay growing regions. As with the Bundaberg model, historical Landsat imagery was acquired over a 12 year (Tully) and 10 year (Mackay) period with the capture window again specified to be between 1st November to 30th June to coincide with the sugarcane growing season. All Landsat images were downloaded and processed using Python programing to automate image processing and data extraction. This allowed the model to be applied rapidly over large areas. For each region, the average green normalized difference vegetation index (GNDVI) for all sugarcane crops was extracted from each image and overlayed onto one time scale 1st November to 30th June. Using the quadratic model derived from each regional data set, the maximum GNDVI achieved for each season was calculated and regressed against the corresponding annual average regional sugarcane yield producing strong correlation for both Tully (R2 = 0.89 and RMSE = 5.5 t/ha) and Mackay (R2 = 0.63 and RMSE = 5.3 t/ha). Moreover, the establishment of an annual crop growth profile from each quadratic model has enabled a benchmark of historic crop development to be derived. Any deviation of future crops from this benchmark can be used as an indicator of widespread abiotic or biotic constraints. As well as regional forecasts, the yield algorithms can also be applied at the pixel level to allow individual yield maps to be derived and delivered near real time to all Australian growers and millers.

Sugarcane yield prediction is critical for in season crop management and decision making processes such as harvest scheduling, storage and milling, and forward selling. This presentation reports on a recently published method of predicting sugarcane yield in the Bundaberg region (Qld) using time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015), were sourced for this study. The images were masked using the field boundary layer vector files of each year and the green normalized difference vegetation index (GNDVI), an indicator of crop vigour was calculated. An analysis of average GNDVI values from all sugarcane crops grown within the Bundaberg region over the 15 year period using a quadratic model identified the beginning of April as the peak growth stage and, therefore, the decisive time of image capture for a single satellite image based yield forecasting. The model derived maximum GNDVI was regressed against historical sugarcane yield data obtained from the mill. The coefficient of determination showed a significant relation between the predicted and actual sugarcane yield (t/ha) with R2 = 0.69 and RMSE 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible technique to predict sugarcane yield in Bundaberg region. This research, however, warrants further investigation to improve and develop accurate operational sugarcane yield prediction model across other domestic and global growing regions, as the influence of environmental conditions and cropping practices will likely vary the relationship between GNDVI and yield (t/ha).

Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R² = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.

To understand fouling in sugar juice heat exchangers, it is first necessary to look at the sugar manufacturing process as a whole (Figure 8.1), where raw sugar is produced from sugarcane. The first step in the process is the harvesting and transport of sugarcane to the mills, where the cane is shredded and then crushed on the milling train to squeeze out the juice. The leftover plant material is called bagasse, and it is used to fire the boilers to create process steam and electricity. After the cane is crushed, the juice is heated to ~76°C, incubated to remove starch and then heated to 100°C, and clarified to remove soluble and insoluble impurities, such as plant material, dirt, and scale-forming ions. During clarification, calcium hydroxide (as milk of lime or lime saccharate) is added to raise the pH (from ~5.4 to ~7.6-7.8) and to form calcium phosphate with the phosphate already present in the juice. If the phosphate levels in the juice are low and the juice is poorly clarifying, phosphoric acid can be added. Calcium phosphate is used to remove both soluble and insoluble impurities, and it forms flocs in the process. A sodium polyacralyate copolymer is added to aid the flocculation of the calcium phosphate flocs. Once the juice is clarified, it is drawn from the top of the clarifier and sent to the evaporators, where the clarified juice is concentrated from 12 to 65 wt% sucrose to form syrup. Within each evaporator unit, the juice is boiled in stainless steel tubes heated by circulating steam in a multieffect arrangement, and approximately 40% of the tube height is submerged in sugar juice during processing. The juice typically passes through five such evaporator units before being transferred to crystallization pans. The evaporators are where the majority of the fouling occurs.

Satellite imagery has been demonstrated to be an effective technology for producing accurate pre-harvest estimates in many agricultural crops. For Australian sugarcane, yield forecasting models have been developed from a single date SPOT satellite image acquired around peak crop growth. However, a failure to acquire a SPOT image at this critical growth stage, from continued cloud cover or from competition for the satellite, can prevent an image being captured and therefore a forecast being made for that season. In order to reduce the reliance on a single image capture and to improve the accuracies of the forecasts themselves, time series yield prediction models have been developed for eight sugarcane growing regions using multiple years of free Landsat satellite images. In addition to the forecasting of average regional yield, an automated computational and programming procedure enabling the derivation of crop vigour variability (GNDVI) maps from the freely available Sentinel 2 satellite imagery was developed. These maps, produced for 15 sugarcane growing regions during the 2017 growing season, identify both variations in crop vigour across regions and within every individual crop. These outputs were made available to collaborating mills within each growing region. This paper presents the accuracies achieved from the time series yield forecasting models versus actual 2017 yields for the respective regions, as well as provides an example of the derived mapping outputs.

The greyback canegrub ('Dermolepida albohirtum') is the main pest of sugarcane crops in all cane-growing regions between Mossman (16.5°S) and Sarina (21.5°S) in Queensland, Australia. In previous years, high infestations have cost the industry up to $40 million. However, identifying damage in the field is difficult due to the often impenetrable nature of the sugarcane crop. Satellite imagery offers a feasible means of achieving this by examining the visual characteristics of stool tipping, changed leaf color, and exposure of soil in damaged areas. The objective of this study was to use geographic object-based image analysis (GEOBIA) and high-spatial resolution GeoEye-1 satellite imagery for three years to map canegrub damage and develop two mapping approaches suitable for risk mapping. The GEOBIA mapping approach for canegrub damage detection was evaluated over three selected study sites in Queensland, covering a total of 254 km² and included five main steps developed in the eCognition Developer software. These included: (1) initial segmentation of sugarcane block boundaries; (2) classification and subsequent omission of fallow/harvested fields, tracks, and other non-sugarcane features within the block boundaries; (3) identification of likely canegrub-damaged areas with low NDVI values and high levels of image texture within each block; (4) the further refining of canegrub damaged areas to low, medium, and high likelihood; and (5) risk classification. The validation based on field observations of canegrub damage at the time of the satellite image capture yielded producer's accuracies between 75% and 98.7%, depending on the study site. Error of commission occurred in some cases due to sprawling, drainage issues, wind, weed, and pig damage. The two developed risk mapping approaches were based on the results of the canegrub damage detection. This research will improve decision making by growers affected by canegrub damage.