Experimental and Mathematical Models for Real-Time Monitoring and Auto Watering Using IoT Architecture

Manufacturing industries based on Internet of Things (IoT) technologies play an important role in the economic development of intelligent agriculture and watering. Water availability has become a global problem that afflicts many countries, especially in remote and desert areas. An efficient irrigation system is needed for optimizing the amount of water consumption, agriculture monitoring, and reducing energy costs. This paper proposes a real-time monitoring and auto-watering system based on predicting mathematical models that efficiently control the water rate needed. It gives the plant the optimal amount of required water level, which helps to save water. It also ensures interoperability among heterogeneous sensing data streams to support large-scale agricultural analytics. The mathematical model is embedded in the Arduino Integrated Development Environment (IDE) for sensing the soil moisture level and checking whether it is less than the pre-defined threshold value, then plant watering is performed automatically. The proposed system enhances the watering system’s efficiency by reducing the water consumption by more than 70% and increasing production due to irrigation optimization. It also reduces the water and energy consumption amount and decreases the maintenance costs.

Simulation of Scale Inhibitor Squeeze Treatments in a Polymer Flooded Reservoir

This study aims to demonstrate the changes to scale inhibitor squeeze lifetimes in a polymer flooded reservoir versus a water flooded reservoir. A squeeze campaign was designed for the base water flood system, then injection was switched to polymer flooding at early and late field life. The squeeze design strategy was adapted to maintain full scale protection under the new system. During the field life, the production of water is a constant challenge. Both in terms of water handling, but also the associated risk of mineral scale deposition. Squeeze treatment is a common technique, where a scale inhibitor is injected to prevent the formation of scale. The squeeze lifetime is dictated by the adsorption/desorption properties of the inhibitor chemical, along with the water rate at the production well. The impact on the adsorption properties and changes to water rate on squeeze lifetime during polymer flooding are studied using reservoir simulation. A two-dimensional 5-spot model was used in this study, considered a reasonable representation of a field scenario, where it was observed that when applying polymer (HPAM) flooding, with either a constant viscosity or with polymer degradation, the number of squeeze treatments was significantly reduced as compared to the water flood case. This is due to the significant delay in water production induced by the polymer flood. When the polymer flood was initiated later in field life, 0.5PV (reservoir pore volumes) of water injection, water cut approximately 70%, the number of squeeze treatments required was still lower than the water flood base case. However, it was also observed that in all cases, at later stages of field life the positive impacts of polymer flooding on squeeze lifetime begin to diminish, due in part to the high viscosity fluid now present in the production near-wellbore region. This study represents the first coupled reservoir simulation/squeeze treatment design for a polymer flooded reservoir. It has been demonstrated that in over the course of a field lifetime, polymer flooding will in fact reduce the number of squeeze treatments required even with a potential reduction in inhibitor adsorption. This highlights an opportunity for further optimization and a key benefit of polymer flooding in terms of scale management, aside from the enhanced oil recovery.

HA-Net: A Lake Water Body Extraction Network Based on Hybrid-scale Attention and Transfer Learning

Due to the large quantity of noise and complex spatial background of the remote sensing images, how to improve the accuracy of semantic segmentation has become a hot topic. Lake water body extraction is crucial for disaster detection, resource utilization, and carbon cycle, etc. The the area of lakes on the Tibetan Plateau has been constantly changing due to the movement of the Earth’s crust. Most of the convolutional neural networks used for remote sensing images are based on single-layer features for pixel classification while ignoring the correlation of such features in different layers. In this paper, the two-branch encoder is presented, which is a multiscale structure that combines the features of ResNet-34 with a feature pyramid network. Secondly, adaptive weights are distributed to global information using the hybrid-scale attention block. Finally, PixelShuffle is used to recover the feature maps’ resolution, and the densely connected block is used to refine the boundary of the lake water body. Likewise, we transfer the best weights which are saved on the Google dataset to the Landsat-8 dataset to ensure that our proposed method is robust. We validate the superiority of Hybrid-scale Attention Network (HA-Net) on two given datasets, which were created by us using Google and Landsat-8 remote sensing images. (1) On the Google dataset, HA-Net achieves the best performance of all five evaluation metrics with a Mean Intersection over Union (MIoU) of 97.38%, which improves by 1.04% compared with DeepLab V3+, and reduces the training time by about 100 s per epoch. Moreover, the overall accuracy (OA), Recall, True Water Rate (TWR), and False Water Rate (FWR) of HA-Net are 98.88%, 98.03%, 98.24%, and 1.76% respectively. (2) On the Landsat-8 dataset, HA-Net achieves the best overall accuracy and the True Water Rate (TWR) improvement of 2.93% compared to Pre_PSPNet, which proves to be more robust than other advanced models.

Increase of energy efficiency ratio of a direct evaporative cooler by dynamic behavior with energy and exergy analysis

The present research is about increasing the energy efficiency ratio (EER) in current direct evaporative coolers (DEC) in Iran. Increasing the cooling load and reducing the electrical energy consumption simultaneously (increasing the energy efficiency ratio (EER)) in DEC are the main goals of manufacturers and consumers of this device. When the circulation water pump runs continuously (static state), the circulation water rate is about 1.89 to 2.90 times of the amounts recommended in the reasonable standards. In order to adjust the circulation water rate to the recommended amount by standards, the present study has utilized repetitive cyclic scheduling programs to reduce the circulation rate to the optimal amount, (by turning the circulation pump on and off by dynamic pattern operation). In other words, the circulation pump stays on only for a certain period of a working cycle, and then the pump stays off for the rest of it. The cooling load and EER were measured based on ASHRAE 133 (2015). The results indicated that the cooling load in the dynamic state increased by 5.03 and 6.18 percent compared to the static state at low and high fan speeds, respectively. Moreover, in comparison with the static state, the amount of electrical energy consumed (kW-hr) in the dynamic state decreased by 8.8 and 4.2 percent at low and high fan speeds, respectively. Finally, the coefficient of performance (COP or EER) of the DEC in the dynamic state is increased by 15.16 and 10.78 in comparison with the static state at low and high fan speeds, respectively.

Smarter Robotic Sprayer System for Precision Agriculture

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

Aplikasi Karbon Aktif dari Cangkang Kelapa Sawit Sebagai Adsorben pada Minyak Jelantah Bahan Sabun

Standart soap quality in SNI 06-3532-1994 is a max water content of 15%, max free fatty acids 2.5% of the number of soaps 196-206 mg/gr in addition there are quality parameters that are not included in the SNI is many foams. Meanwhile, some industrial companies in Indonesia attract consumers with a lot of foam produced by the company's products.The purpose of this study is to discuss the effect of the size of the active carbon particles of palm shells on the reduction of free fatty acids in jelantah oil as the manufacture of soap raw materials and soap quality. determination of Free Fatty Acids using AOCS Ca 5a-40-1997 method, testing moisture content using SNI method 06-3532-1994, The number of hoardings uses the volumetric method. The results of this study showed that particle sizes of 100 mesh and 120 mesh occurred significant differences in the decline of free fatty acids. Where the particle size of 120 mesh is stronger absorbency in lowering Free Fatty Acids. this is because materials with smaller diameters have a greater absorption rate. This is related to the surface area of the activated carbon adsorbent of the palm shell that is available to interact with the substance contained in the solution. But there is no significant difference in the quality of soap, namely; Water Rate, Lots of Foam, Number of Hoarding.

Effect of dental intervention on improvements in metabolic syndrome patients: a randomized controlled clinical trial

Background Metabolic syndrome (MetS), caused by the accumulation of visceral fat, is considered a major cause of cardiovascular disease. This randomized controlled trial aimed to clarify the effect of dental intervention, including prosthodontics and/or periodontal treatment, combined with dietary and exercise guidance on MetS. Methods In total, 112 patients who met the Japanese waist circumference criteria of MetS were recruited. The intervention group (ITG) received dental intervention along with dietary and exercise guidance, while the control group (CTG) received dietary and exercise guidance alone. Three outcome measurements were obtained before intervention (BL), 1 month after intervention (1M), and 3 months after intervention (3M). Results Body water rate (p = 0.043) was significantly higher in ITG than in CTG at 1M. Simultaneously, fasting blood sugar level (p = 0.098) tended to be lower in ITG than in CTG. Lean mass (p = 0.037) and muscle mass (p = 0.035) were significantly higher and body weight (p = 0.044) significantly lower in ITG than in CTG at 3M. Body mass index (p = 0.052) tended to be lower in ITG than in CTG. Conclusions Dental intervention combined with lifestyle guidance may improve anthropometric status and reduce the risk of MetS. Trial registration University Hospital Medical Information Network Center Unique UMIN000022753. https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000026176.

Average Fracture Compressibility from Flowback Data

Summary We propose a novel method for estimating average fracture compressibility cf¯ during flowback process and apply it to flowback data from 10 multifractured horizontal wells completed in Woodford (WF) and Meramec (MM) formations. We conduct complementary diagnostic flow-regime analyses and calculate cf¯ by combining a flowing-material-balance (FMB) equation with pressure-normalized-rate (PNR)-decline analysis. Flowback data of these wells show up to 2 weeks of single-phase water production followed by hydrocarbon breakthrough. Plots of water-rate-normalized pressure and its derivative show pronounced unit slopes, suggesting boundary-dominated flow (BDF) of water in fractures during single-phase flow. Water PNR decline curves follow a harmonic trend during single-phase- and multiphase-flow periods. Ultimate water production from the forecasted harmonic trend gives an estimate of initial fracture volume. The cf¯ estimates for these wells are verified by comparing them with the ones from the Aguilera (1999) type curves for natural fractures and experimental data. The results show that our cf¯ estimates (4 to 22×10−5 psi−1) are close to the lower limit of the values estimated by previous studies, which can be explained by the presence of proppants in hydraulic fractures.

Scaling tendency prediction in water injection well of K2 formation of C68 block

The K2 formation of C68 block is explored by injecting water to maintain formation pressure, but the continuous decrease of injection rate significantly reduces oil production. Therefore, it is important to predict scaling tendency of injected water in the formation. Firstly, ion composition of formation water and injected water was tested according to recommended practices in petroleum industry. Then, wellbottom temperature distribution of injection wells was simulated under injection water rate requirement of oilfield development. Furthermore, based on the “Oddo-Tomson” prediction model of inorganic scale, the scaling trend of water flooding in K2 formation is predicted according to the possible temperature and pressure. The research indicates that sulfate scale cannot be formed in C68 block and there is a slight possibility of carbonate scaling, which provides a basis to select the correct stimulation technology for increasing production.