Fertilizer deposition as a function of angular velocity and inclination of the helical dosing mechanism

ABSTRACT The establishment of grain crops in Brazil is an important industrial process in the agricultural chain, requiring the correct deposition of granular fertilizer over the sowing furrow and more efficient, precise, and sustainable assessments in the operation, which can be achieved with the statistical process control. This study aimed to assess the effect of the angular velocity on different inclinations of the helical metering mechanism on the granular fertilizer deposition. An automated electronic bench was used to assess the deposition quality of granular fertilizers considering different angular velocities (1.11, 1.94, and 2.77 m s-1) and longitudinal and transverse inclinations (+15, +7.5, 0, −7.5, and −15°), with the helical doser by overflow. Flow data were collected and submitted to descriptive statistics and statistical process control. The metering mechanism showed expected variations, with acceptable performance under process control. The values of the flow rates of the granular fertilizer increased as velocity increased, standing out longitudinal inclinations of +7.5 and +15°, providing higher fertilizer depositions.

Reducing Unnecessary Respiratory Viral Testing to Promote High-Value Care

BACKGROUND AND OBJECTIVES: Viral respiratory infections are common in children, and practice guidelines do not recommend routine testing for typical viral illnesses. Despite results often not impacting care, nasopharyngeal swabs for viral testing are frequently performed and are an uncomfortable procedure. The aim of this initiative was to decrease unnecessary respiratory viral testing (RVT) in the emergency department (ED) and the pediatric medicine wards (PMWs) by 50% and 25%, respectively, over 36 months. METHODS: An expert panel reviewed published guidelines and appropriate evidence to formulate an RVT pathway using plan-do-study-act cycles. A multifaceted improvement strategy was developed that included implementing 2 newer, more effective tests when testing was deemed necessary; electronic order modifications with force functions; audit and feedback; and education. By using statistical process control charts, the outcomes analyzed were the percentage of RVT ordered in the ED and the rate of RVT ordered on the PMWs. Balancing measures included return visits leading to admission and inpatient viral nosocomial outbreaks. RESULTS: The RVT rate decreased from a mean of 3.0% to 0.5% of ED visits and from 44.3 to 30.1 per 1000 patient days on the PMWs and was sustained throughout the study. Even when accounting for the new rapid influenza test available in the ED, a 50% decrease in overall ED RVT was still achieved without any significant impact on return visits leading to admission or inpatient nosocomial infections. CONCLUSIONS: Through implementation of a standardized, electronically integrated RVT pathway, a decrease in unnecessary RVT was successfully achieved. Audit and feedback, reminders, and biannual education all supported long-term sustainability of this initiative.

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

Quality improvement methodology can reduce hospitalisation for abscess management

BackgroundAbscesses are a common reason for ED visits. While many are drained in the ED, some require drainage in the operating room (OR). We observed that a higher percentage of patients at our institution in Columbus, Ohio, were admitted to the hospital with abscesses for incision and drainage (I&D) in the OR than other institutions, including paediatric institutions. Our aim was to decrease hospitalisations for abscess management.MethodsA multidisciplinary team convened to decrease hospitalisation for patients with abscesses and completed multiple ‘Plan-Do-Study-Act’ cycles, including increasing I&Ds performed in the ED. Other interventions included implementation of a clinical pathway, training of procedure technicians (PT), updating the electronic medical record (EMR), credentialing advanced practice nurses in sedation and individual follow-up with providers for admitted patients. Data were analysed using statistical process control charts. Gross average charges were assessed.ResultsAdmissions for I&D decreased from 26.3% to 13.7%. Abscess drainage in the ED improved from 79.3% to 96.5%. Mean length of stay decreased from 19.5 to 11.5 hours for all patients. Patients sedated increased from 3.3% to 18.2%. The number of repeat I&Ds within 30 days decreased from 4.3% to 1.7%.ConclusionWe decreased hospitalisations for abscess I&D by using quality improvement methodology. The most influential intervention was an initiative to increase I&Ds performed in the ED. Additional interventions included expanded training of PTs, implementation of a clinical pathway, updating the EMR, improving interdepartmental communication and increasing sedation providers.

Analisis Pengendalian Kualitas Crude Palm Oil (CPO) PT. Kampar Tunggal Agrindo Dengan Menggunakan Statistical Process Control

Kampar Tunggal Agrindo merupakan perusahaan yang bergerak dibidang Pabrik Kelapa Sawit berskala 15 T/H berasal dari Kabupaten Kampar mengolah buah brondolan sawit menjadi minyak mentah (crude palm oil). Perusahaan ini mengalami ketidaksesuaian standart hasil produksi yang ditetapkan oleh perusahaan, dengan standar kadar asam < 30%, kadar air <0,50%, kadar kotoran <0,030%. Maka dari itu dibutuhkan analisis menggunakan metode statistical process control. Dari 3 parameter analisis ketidaksesuaian standart produk crude palm oil (CPO), diparamater kadar asam dan kadar air ada beberapa data yang out of control yang berarti tidak terkendali dan harus dilakukan analisis lebih lanjut. Dari 3 parameter kualitas produk CPO, maka yang menjadi prioritas untuk diperbaiki adalah parameter kadar air. Akar penyebab dari tingginya kadar air (> 0,50%) adalah manusia antara lain kurangnya inspeksi pada waktu perebusan akibatnya tekanan steam uap berlebihan sehingga suhu terlalu panas akibat kurang komunikasi antara operator boiler dan perebusan. Untuk itu perlu dilakukan pengawasan SOP dari pihak kepala produksi terhadap operator, penambahan unit material handling berupa loader dan merancang checklist preventif untuk mencegah kerusakan.

Drift Compensation of the Electronic Nose in the Development of Instruments for Out-of-Laboratory Analysis

A technique was developed to evaluate and compensate for the drift of eight mass-sensitive sensors in an open detection cell in order to estimate the influence of external factors (temperature, changes in the chemical composition of the background) on the out-of-laboratory analysis of biosamples. The daily internal standardization of the system is an effective way to compensate for the sensor signal drift when the sorption properties of sensitive coatings change during their long-term, intensive operation. In this study, distilled water was proposed as a standard for water matrix-based biosamples (blood, exhaled breath condensate, urine, etc.). Further, internal standardization was based on daily calculation of the specific sensor signals by dividing the sensor signals for the biosample according to the corresponding averaged values obtained from three to five standard measurements. The stability of the sensor array operation was estimated using the theory of statistical process control (exponentially weighted moving average control charts) based on the specific signal of the sensor array. The control limits for the statistical quantity of the central tendency for each sensor and the whole array, as well as the variations of the sensor signals, were determined. The average times required for signal and run lengths, for the purpose of statistically substantiated monitoring of the electronic nose’s stability, were calculated. Based on an analysis of the tendency and variations in sensor signals during 3 months of operation, a technique was formulated to control the stability of the sensor array for the out-of-laboratory analysis of the biosamples. This approach was successfully verified by classifying the results of the analysis of the blood and water samples obtained for this period. The proposed technique can be introduced into the software algorithm of the electronic nose, which will improve decision-making during the long-term monitoring of health conditions in humans and animals.