Current Monitoring Technologies Research Articles

Predicting main behaviors of beef bulls from accelerometer data: A machine learning framework

Traditional methods to monitor free-range cattle, such as breeding beef bulls, are time-consuming. However, most current remote monitoring technologies operate at high sampling rates, making their use on bulls impractical due to their high battery consumption. Therefore, this study aims to describe and evaluate a machine-learning framework to predict the behaviors of beef bulls from raw accelerometer data at low sampling rates. Collars with 3D-accelerometers were deployed on 33 bulls, recording accelerometer data at 0.5 Hz (22 bulls in 2020 and 2021) or 1.0 Hz (11 bulls in 2023). Videos of bulls in pens, synched with the accelerometer by time, were recorded and analyzed. The behaviors investigated were grazing (GR), resting (RE), ruminating (RU), walking (WA), and in 2023, fighting (FI). Primary labels of activity (AC), corresponding to GR, WA, and FI, and non-activity (NA), corresponding to RE and RU, were assigned. Two datasets were created from data sampled at 1.0 Hz and 0.5 Hz. Then, behavioral events with duration within the inferior 0.05 quantile of the distribution for each behavior were removed, integrated measures of motion were calculated, and segmentation into consecutive 20s time-windows was performed. Afterward, 132 frequency and time-domain features were extracted, and bulls’ ages were added as a physical feature. Two bulls from each year and dataset were segregated to form independent test sets. A leave-one-animal-out cross-validation (LOAO) was applied to Extratree classifiers to select relevant features. The final classifier was built in a hierarchical structure using XGBoost classifiers to make predictions on two levels: (1) distinguishing between AC and NA, and (2) categorizing AC into GR, WA, FI, and NA into RE or RU. This model was evaluated using LOAO and test sets for each dataset, and precision and sensitivity were calculated for each behavior. Matthews Correlation (MCC) and Cohen's Kappa (CK) coefficients were calculated for the overall assessment of the models’ levels. Comparisons of metrics obtained on LOAO and test sets were performed using the Wilcoxon Sum Rank and the Wilcoxon Signed Rank test. The LOAO MCC for 1.0 Hz (1st level=0.98±0.01, 2nd level=0.92±0.02) was higher than 0.5 Hz (1st level=0.83±0.20, 2nd level=0.71±0.20). In 1.0 Hz, all behaviors presented mean precision and sensitivity above 0.7, except the sensitivity of FI (LOAO = 0.47±0.06, test set = 0.63±0.18). In 0.5 Hz, the exception was the sensitivity of WA (LOAO = 0.58±0.28, test set = 0.68±0.06) and the sensitivity of RU in the test set (0.54 ± 0.26). Therefore, the proposed framework can be used to predict the behaviors of beef bulls from accelerometers sampling at 0.5 Hz or 1.0 Hz, although better results are observed at 1.0 Hz. Caution should be exercised for predicting FI at 1.0 Hz and WA at 0.5 Hz.

The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process

Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted.

Technology Gaps for Monitoring Birds and Marine Mammals at Offshore Wind Facilities

Abstract With increased focus on offshore wind (OSW) as a renewable energy resource in the United States and elsewhere, there are concerns about OSW impacts to wildlife, particularly birds and marine mammals. This study identifies technology gaps and technological research and development (R&D) priorities for monitoring marine mammals and birds for fixed and floating OSW. A synthesis of current monitoring technologies generated two databases (with over 100 technologies) that can be integrated in current technology repositories for renewable energy projects. Generally, the key technology R&D needs are similar for birds and marine mammals. The main exception is that some types of bird technologies are more likely to require direct integration with OSW infrastructure, whereas marine mammal systems tend to operate independently. Priorities to advance wildlife monitoring include improved early communication, harmonization of technologies and data collection for monitoring systems on OSW structures, battery/power access improvements, remote data transfer improvements, and advancements in automated collection and analysis of data. The successful integration of wildlife monitoring systems into OSW infrastructure and operations is dependent on remote access mechanisms for data collection, system maintenance, and data transfer, in order to minimize risks to worker safety in the offshore environment, as well as minimizing costs and disruption to normal operational activities. Application of the results of this study to prioritize and fund technology R&D will help to support statistically robust data collection and practicable integration of monitoring systems into OSW operations and infrastructure.

Optimizing microseismic monitoring: a fusion of Gaussian–Cauchy and adaptive weight strategies

Abstract In mining mineral resources, it is vital to monitor the stability of the rock body in real time, reasonably regulate the area of ground pressure concentration, and guarantee the safety of personnel and equipment. The microseismic signals generated by monitoring the rupture of the rock body can effectively predict the rock body disaster, but the current microseismic monitoring technology is not ideal. In order to address the issue of microseismic monitoring in deep wells, this research suggests a machine learning-based model for predicting microseismic phenomena. First, this work presents the random spare, double adaptive weight, and Gaussian–Cauchy fusion strategies as additions to the multi-verse optimizer (MVO) and suggests an enhanced MVO algorithm (RDGMVO). Subsequently, the RDGMVO-Fuzzy K-Nearest Neighbours (RDGMVO-FKNN) microseismic prediction model is presented by combining it with the FKNN classifier. The experimental section compares 12 traditional and recently enhanced algorithms with RDGMVO, demonstrating the latter’s excellent benchmark optimization performance and remarkable improvement effect. Next, the FKNN comparison experiment, the classical classifier experiment, and the microseismic dataset feature selection experiment confirm the precision and stability of the RDGMVO-FKNN model for the microseismic prediction problem. According to the results, the RDGMVO-FKNN model has an accuracy above 89%, indicating that it is a reliable and accurate method for classifying and predicting microseismic occurrences. Code has been available at https://github.com/GuaipiXiao/RDGMVO.

A Review of Research on the Application and Development of Meteorological Monitoring in the Field of Agriculture

The application of meteorological monitoring technology in the agricultural field has become an indispensable and important component of agricultural production and management processes.In this paper, the application of meteorological monitoring in the agricultural field is summarized,including the types of current meteorological monitoring technologies,data acquisition and processing methods,as well as their practical application effects in different agricultural environments,including meteorological stations,satellite remote sensing,unmanned aerial vehicles(UAVs),and sensor networks [4].Then,aspects of meteorological data acquisition and processing methods,data collection,transmission,and storage are explored.Next,the application effects of meteorological monitoring technology in crop cultivation,yield estimation, plant diseases and insect pests,livestock husbandry,and greenhouse agriculture are discussed in detail for different types of agricultural environments.Finally,the development trends and future research directions of meteorological monitoring technology are discussed.

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

The service environment of offshore wind turbine (OWT) support structures is harsh, and it is extremely difficult to replace these structures during their operational lifespan, making their failure a catastrophic event. The structural health monitoring (SHM) of OWT support structures is a crucial aspect of operational maintenance for OWT support structures, aiming to mitigate significant financial losses. This paper systematically summarizes the current monitoring methods and technologies for OWT support structures, including towers and foundations. Through the review of monitoring content and the evolution of monitoring techniques for supporting structures, it delves deeper into the challenges faced by wind turbine monitoring and highlights potential avenues for future development. Then, the current damage identification techniques for OWT towers and foundations are analyzed, exploring various methods including model-based, vibration-based, artificial intelligence and hybrid fault diagnosis methods. The article also examines the advantages and disadvantages of each approach and outlines potential future directions for research and development in this field. Furthermore, it delves into the current damage identification techniques for OWT towers and foundations, discussing prevalent challenges and future directions in this domain. This status review can provide reference and guidance for the monitoring design of OWT support structures, and provide support for the fault diagnosis of OWT support structures.

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R2 increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R2 of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research.

Multi-stage Dynamic Latent Variable Model and Lightweight PCANet Based Anomaly Identification of Fused Magnesium Furnaces

The fused magnesium furnace (FMF) is a crucial component in the production of magnesium. The inability to timely detect abnormal operation can result in a semi-molten state, posing a serious risk to production safety. The limitations of current monitoring technologies hinder effective anomaly detection due to their failure to extract intrinsic data features and fully utilize multi-source information. This paper introduces a novel multi-source information fusion approach for anomaly identification of FMFs, leveraging dynamic feature extraction. A two-layer multi-stage dynamic latent variable model is established for dynamic feature extraction. Meanwhile, a lightweight principal component analysis network (PCANet) is established for video deep-learning online identification. The lightweight model can improve the recognition efficiency and reduce the computational cost. Subsequently, through decision-level fusion of multi-source information, the final diagnostic outcomes facilitate the classification and identification of FMF operating conditions. Experiments on real industrial data substantiate the efficacy and advancement of the proposed method. The final accuracy reached 98.43%.

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review.

The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.

Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique

Respiratory diseases have been increasingly affecting people worldwide, posing a major public health challenge due to rising morbidity and mortality rates. Subtle abnormal respiratory sounds s may present early in the pulmonary or respiratory tract diseases, therefore urging an instant intervention in critical clinical conditions. However, the current monitoring and signal analysis technologies pose significant challenges in achieving real-time, convenient, and accurate respiratory disease monitoring. Here, we propose a novel automatic auxiliary diagnosis system that utilizes a flexible electret-based self-powered sensor (FESS), signal processing technology, and machine learning algorithms. The FESS is based on a strain-enhanced laminated electret with an edge-to-edge hollow hemisphere array structure, which enables the system to detect and prevent various respiratory diseases with high accuracy rates of 99.43%, sensitivity of 98.30%, and specificity of 99.02%. Our system holds immense potential in reducing medical burden and improving the overall health of individuals.

Intracranial Pressure Monitoring and Treatment Thresholds in Acute Neural Injury: A Narrative Review of the Historical Achievements, Current State, and Future Perspectives.

Since its introduction in the 1960s, intracranial pressure (ICP) monitoring has become an indispensable tool in neurocritical care practice and a key component of the management of moderate/severe traumatic brain injury (TBI). The primary utility of ICP monitoring is to guide therapeutic interventions aimed at maintaining physiological ICP and preventing intracranial hypertension. The rationale for such ICP maintenance is to prevent secondary brain injury arising from brain herniation and inadequate cerebral blood flow. There exists a large body of evidence indicating that elevated ICP is associated with mortality and that aggressive ICP control protocols improve outcomes in severe TBI patients. Therefore, current management guidelines recommend a cerebral perfusion pressure (CPP) target range of 60-70 mm Hg and an ICP threshold of >20 or >22 mm Hg, beyond which therapeutic intervention should be initiated. Though our ability to achieve these thresholds has drastically improved over the past decades, there has been little to no change in the mortality and morbidity associated with moderate-severe TBI. This is a result of the "one treatment fits all" dogma of current guideline-based care that fails to take individual phenotype into account. The way forward in moderate-severe TBI care is through the development of continuously derived individualized ICP thresholds. This narrative review covers the topic of ICP monitoring in TBI care, including historical context/achievements, current monitoring technologies and indications, treatment methods, associations with patient outcome and multi-modal cerebral physiology, present controversies surrounding treatment thresholds, and future perspectives on personalized approaches to ICP-directed therapy.

Smartcaptor Intelligent Police System Based on Multi-Technology Integration

With the rapid development of China's national economy, urban and rural transportation activities are increasingly frequent. At the same time, road traffic accidents have become a major problem affecting social development and people's lives. This paper is based on the SmartCaptor intelligent police project system, edge computing, 5G, IoT and artificial intelligence and other technologies for integrated innovation, a good solution to the current road monitoring technology bottleneck, to achieve a device multiple functions, equipment cost is low and other core competitive advantages, SmartCaptor intersection intelligent police have full competitive advantage and market prospects.

Established and Emerging Research Trends in Norway Lobster, Nephrops norvegicus.

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies.

A new real-time debris flow and avalanches detection system based on optical fiber sensing

The real-time detection of potentially destructive water, earth and snow mixtures such as debris flow and avalanches is a topic of growing interest to mitigate the risk in anthropized areas such as the Alpine region. In view of this, a new cutting-edge debris flow and avalanche detection system, called OPTIALP, was developed. The proposed system exploits the polarization variations induced on the fiber by mechanical vibrations, for the automatic detection along their propagation path of potentially destructive snow and soil-water mixtures. One of the main values-add of the OPTIALP system is the “quasi distributed” and spatially continuous detection along the whole fiber which improves the current monitoring technologies relying on “discrete” monitoring points and sensors. The OPTIALP system was designed and thoroughly tested in the laboratory environment by means of a specific setup. Over 650 tests were carried out and a new signal processing algorithm developed in Matlab environment capable to interpret the data acquired was created. The results showed that the OPTIALP system is able to correctly identify the signals produced by lab-scale mass movements.

Self-powered overspeed wake-up alarm system based on triboelectric nanogenerators for intelligent transportation

Overspeed information monitoring and collecting becomes increasingly important with the rapid development of intelligent transportation, but current overspeed monitoring technologies are constrained by traditional power-supply for large-scale use. Herein, we proposed a self-powered overspeed wake-up alarm system (SOWAS) based on triboelectric nanogenerators. The SOWAS consists of an energy harvesting triboelectric nanogenerator (E-TENG), an energy management module (EMM), an overspeed sensing triboelectric nanogenerator (S-TENG), a power-switch module (PSM) and a wireless transceiver module (WTM). The E-TENG and EMM are used for mechanical energy harvesting, management, and powering for the SOWAS, while the S-TENG, PSM and WTM are employed for active overspeed monitoring and alarm signals transmitting. When the vehicle speed exceeds the setting threshold, the PSM will be activated, and the WTM will send alarm signals with the harvested energy. The response characteristics dependence of the SOWAS on various working parameters were investigated in detail. The developed SOWAS could work sustainably in unattended traffic environment without external power supply for autonomous overspeed monitoring and alarming. Meanwhile, intelligent monitoring of overspeed and improved energy utilization were achieved. This work has enormous potentials and promising prospects for TENG in intelligent transportation, Internet of Vehicles and autonomous driving.

Telemedicine and Remote Rehabilitation for Patients with Cardiac Disease Post COVID-19 Era

Background The COVID-19 pandemic has accelerated the changes of daily practice in medical care worldwide. Telemedicine is one of the most stimulated fields among these changes, even in the cardiovascular disease care. In the past decade, remote monitoring has become an important tool that provides valuable clinical information via respiratory assist devices and implantable pacemakers from patients at home. Objective The objective of our study was to investigate whether these remote monitoring approaches could include a broader spectrum of patients with cardiac disease with the development of noninvasive monitoring devices. Methods Several pilot studies of telemedicine-based monitoring systems using commercially available digital devices were conducted in the infectious disease wards and other clinical settings during the COVID-19 pandemic. We evaluated the effectiveness of a remote heart monitoring system that provides real-time electrocardiogram and other vital signs monitoring in patients with cardiovascular disease. A newly developed remote cardiac rehabilitation system was also evaluated. Results We found that current remote monitoring technology could provide sufficient monitoring of vital signs, suggesting a potential to predict a worsening of heart failure in advance. Remote cardiac rehabilitation could be effectively and safely provided in patients with low to medium risk. Conclusions Telemedicine and remote cardiac rehabilitation possess a great potential in the cardiovascular disease practice post COVID-19 era; however, there are several unsolved issues regarding their implementation in the real-world clinical practice.

Assessing Hand Perfusion With Eulerian Video Magnification and Waveform Extraction

Timely and accurate triage of upper extremity injuries is critical, but current perfusion monitoring technologies have shortcomings. These limitations are especially pronounced in patients with darker skin tones. This pilot study evaluates a Eulerian Video Magnification (EVM) algorithm combined with color channel waveform extraction to enable video-based measurement of hand and finger perfusion. Videos of 10 volunteer study participants with Fitzpatrick skin types III-VI were taken in a controlled environment during normal perfusion and tourniquet-induced ischemia. Videos were EVM processed, and red/green/blue color channel characteristics were extracted to produce waveforms. These videos were assessed by surgeons with a range of expertise in hand injuries. The videos were randomized and presented in 1 of 3 ways: unprocessed, EVM processed, and EVM with waveform output (EVM+waveform). Survey respondents indicated whether the video showed an ischemic or perfused hand or if they were unable to tell. We used group comparisons to evaluate response accuracy across video types, skin tones, and respondent groups. Of the 51 providers to whom the surveys were sent, 25 (49%) completed them. Using the Pearson χ2 test, the frequencies of correct responses were significantly higher in the EVM+waveform category than in the unprocessed or EVM videos. Additionally, the agreement was higher among responses to the EVM+waveform questions than among responses to the unprocessed or EVM processed. The accuracy and agreement from the EVM+waveform group were consistent across all skin pigmentations evaluated. Video-based EVM processing combined with waveform extraction from color channels improved the surgeon's ability to identify tourniquet-induced finger ischemia via video across all skin types tested. Eulerian Video Magnification with waveform extraction improved the assessment of perfusion in the distal upper extremity and has potential future applications, including triage, postsurgery vascular assessment, and telemedicine.

Drone Image Based Illegal Crowd Detection For Covid-19 Disease Prevention Via Convolutional Neural Networks (CNNs) Transfer Learning

COVID-19 originated in Wuhan, China, in December 2019 and quickly became a global outbreak in January 2020. COVID-19 is a disease caused by SARS-CoV-2, which is a human transmission disease. Since it is a human transmission disease, thus mass gathering in public is not allowed to prevent the possible spread of COVID-19. However, the current monitoring technology, such as closed-circuit television (CCTV), only cover a limited area of the public and lack of mobility. Image classification is one of the approaches that can detect crowds in an image and can be done through either machine learning or deep learning approach. Recently, deep learning, especially convolutional neural networks (CNNs) outperform classical machine learning in image classification and the common approach for modelling CNN is through transfer learning. Thus, this study aims to develop a convolutional neural network that can detect illegal crowd gathering from offline drone view images through image classification using the transfer learning technique. Several models are used to train on the same dataset obtained, and the all-model performance is evaluated through a confusion matrix. Based on performance analysis, it shows that the ResNet50 model outperforms the VGG16 model and InceptionV3 model by achieving 95% test accuracy, 95% precision, 95% recall and 95% F1-score. In conclusion, it can be concluded that the deep learning approach uses a pre-trained convolutional neural network that can be used to classify object images in this study.

Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

Assessing the numbers and distribution of at-risk megafauna such as the black rhino (Diceros bicornis) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals (e.g., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework has a lower hardware cost and can function with a reduced internet bandwidth requirement for local network communication. It proposes the use of a Jetson Xavier NX, onboard a Parrot Anafi drone, connected to the internet throughout the flight to deliver a lightweight web-based notification system upon detection of the target species. The GPS location with the detected target species images is sent using MQ Telemetry Transport (MQTT), a lightweight messaging protocol using a publisher/subscriber architecture for IoT devices. It provides reliable message delivery when internet connection is sporadic. We used a YOLOv5l6 object detection architecture trained to identify a bounding box for one of five objects of interest in a frame of video. At an intersection over union (IoU) threshold of 0.5, our model achieved an average precision (AP) of 0.81 for black rhino (our primary target) and 0.83 for giraffe (Giraffa giraffa). The model was less successful at identifying the other smaller objects which were not our primary targets: 0.34, 0.25, and 0.42 for ostrich (Struthio camelus australis), springbok (Antidorcas marsupialis) and human respectively. We used several techniques to optimize performance and overcome the inherent challenge of small objects (animals) in the data. Although our primary focus for the development of the model was rhino, we included other species classes to emulate field conditions where many animal species are encountered, and thus reduce the false positive occurrence rate for rhino detections. To constrain model overfitting, we trained the model on a dataset with varied terrain, angle and lighting conditions and used data augmentation techniques (i.e., GANs). We used image tiling and a relatively larger (i.e., higher resolution) image input size to compensate for the difficulty faced in detecting small objects when using YOLO. In this study, we demonstrated the potential of a drone-based AI pipeline model to automate the detection of free-ranging megafauna detection in a remote setting and create alerts to a wildlife manager in a relatively poorly connected field environment.

Struvite precipitation within wastewater treatment: A problem or a circular economy opportunity?

Enhanced biological phosphorus removal at wastewater treatment plants that use anaerobic digesters for sludge treatment have historically encountered phosphate precipitation problems in the form of struvite. Literature on struvite is thin which is surprising given it can foul/block the sludge return lines and associated pumps and valves, causing significant operational problems. This study has evaluated if a typical large wastewater treatment plant can overcome this problem by adopting circular economy thinking. The struvite profile based on the supersaturation ratio of (Mg:NH4:PO42−), pH and temperature demonstrates the potential operational hotspots that can present uncontrolled struvite formation. Based on current struvite monitoring technologies and a cost-benefit analysis, the controlled struvite recovery via an Ostara crystallization reactor has been demonstrated to be economically viable with a pay-back period of less than a decade. An integrated evaluation illustrates the positive environmental impact arising from the utilisation of the recovered product. Economic viability and payback periods will vary according to circumstances, but we recommend that WWTP operators globally consider fitting a crystallisation reactor to appropriate plants, The outcomes and recommendation from this study are particularly timely given the global fertiliser shortage (2022) that is driving up food prices and reducing crop sizes.