scholarly journals Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

2021 ◽  
Vol 11 (11) ◽  
pp. 5042
Author(s):  
Orhan Can Görür ◽  
Xin Yu ◽  
Fikret Sivrikaya
Keyword(s):  
Planning Process ◽  
Industrial Process ◽  
Conveyor Belt ◽  
Predictive Maintenance ◽  
Process Efficiency ◽  
Support Vector ◽  
Small Scale ◽  
Process Scheduling ◽  
Adaptive Process ◽  
Autonomous Planning

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

scholarly journals Improvement of Biogas Quality and Quantity for Small-Scale Biogas-Electricity Generation Application in off-Grid Settings: A Field-Based Study

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3088
Author(s):  
Henry Wasajja ◽  
Saqr A. A. Al-Muraisy ◽  
Antonella L. Piaggio ◽  
Pamela Ceron-Chafla ◽  
Purushothaman Vellayani Aravind ◽  
...  
Keyword(s):  
Electricity Generation ◽  
Relevant Literature ◽  
Electrical Power ◽  
Process Efficiency ◽  
Electricity Production ◽  
Small Scale ◽  
Buffer System ◽  
Biogas Plants ◽  
Operational Practices ◽  
Pre Treatment

Small-scale electrical power generation (<100 kW) from biogas plants to provide off-grid electricity is of growing interest. Currently, gas engines are used to meet this demand. Alternatively, more efficient small-scale solid oxide fuel cells (SOFCs) can be used to enhance electricity generation from small-scale biogas plants. Most electricity generators require a constant gas supply and high gas quality in terms of absence of impurities like H2S. Therefore, to efficiently use the biogas from existing decentralized anaerobic digesters for electricity production, higher quality and stable biogas flow must be guaranteed. The installation of a biogas upgrading and buffer system could be considered; however, the cost implication could be high at a small scale as compared to locally available alternatives such as co-digestion and improved digester operation. Therefore, this study initially describes relevant literature related to feedstock pre-treatment, co-digestion and user operational practices of small-scale digesters, which theoretically could lead to major improvements of anaerobic digestion process efficiency. The theoretical preamble is then coupled to the results of a field study, which demonstrated that many locally available resources and user practices constitute frugal innovations with potential to improve biogas quality and digester performance in off-grid settings.

A Machine Learning Based Approach of Performance Estimation for High-Pressure Compressor Airfoils

2018 ◽  
Author(s):  
Jonas Marx ◽  
Stefan Gantner ◽  
Jörn Städing ◽  
Jens Friedrichs
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Machine Learning Algorithms ◽  
Performance Estimation ◽  
Predictive Maintenance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Operating Characteristics ◽  
High Pressure Compressor ◽  
Pressure Compressor

In recent years, the demands of Maintenance, Repair and Overhaul (MRO) customers to provide resource-efficient after market services have grown increasingly. One way to meet these requirements is by making use of predictive maintenance methods. These are ideas that involve the derivation of workscoping guidance by assessing and processing previously unused or undocumented service data. In this context a novel approach on predictive maintenance is presented in form of a performance-based classification method for high pressure compressor (HPC) airfoils. The procedure features machine learning algorithms that establish a relation between the airfoil geometry and the associated aerodynamic behavior and is hereby able to divide individual operating characteristics into a finite number of distinct aero-classes. By this means the introduced method not only provides a fast and simple way to assess piece part performance through geometrical data, but also facilitates the consideration of stage matching (axial as well as circumferential) in a simplified manner. It thus serves as prerequisite for an improved customary HPC performance workscope as well as for an automated optimization process for compressor buildup with used or repaired material that would be applicable in an MRO environment. The methods of machine learning that are used in the present work enable the formation of distinct groups of similar aero-performance by unsupervised (step 1) and supervised learning (step 2). The application of the overall classification procedure is shown exemplary on an artificially generated dataset based on real characteristics of a front and a rear rotor of a 10-stage axial compressor that contains both geometry as well as aerodynamic information. In step 1 of the investigation only the aerodynamic quantities in terms of multivariate functional data are used in order to benchmark different clustering algorithms and generate a foundation for a geometry-based aero-classification. Corresponding classifiers are created in step 2 by means of both, the k Nearest Neighbor and the linear Support Vector Machine algorithms. The methods’ fidelities are brought to the test with the attempt to recover the aero-based similarity classes solely by using normalized and reduced geometry data. This results in high classification probabilities of up to 96 % which is proven by using stratified k-fold cross-validation.

scholarly journals A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

2018 ◽  
Author(s):  
Feng Jie Zheng ◽  
Fu Zheng Qu ◽  
Xue Guan Song
Keyword(s):  
Pressure Fluctuation ◽  
Large Scale ◽  
Three Dimensional ◽  
Industrial Process ◽  
Multiscale Model ◽  
Computational Time ◽  
Small Scale ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Proposed Model

Reservoir-pipe-valve (RPV) systems are widely used in many industrial process. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operation such as rapid valve opening/closing. To investigate the pressure especially the pressure fluctuation in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled by a zero-dimensional virtual point, the pipe is modeled by a one-dimensional MOC, and the valve is modeled by a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted, in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve are obtained. The results show that the proposed model is in good agreement with the full CFD model in both large-scale and small-scale spaces. Moreover, the proposed model is more computationally efficient than the CFD model, which provides a feasibility in the analysis of complex RPV system within an affordable computational time.

scholarly journals Looking for Options to Sustainably Fixate Nitrogen. Are Molecular Metal Oxides Catalysts a Viable Avenue?

2021 ◽  
Vol 9 ◽  
Author(s):  
Rebeca González-Cabaleiro ◽  
Jake A. Thompson ◽  
Laia Vilà-Nadal
Keyword(s):  
Metal Oxides ◽  
Carbon Dioxide Emissions ◽  
Triple Bond ◽  
Industrial Process ◽  
Modern Society ◽  
Small Scale ◽  
Scale Production ◽  
Ammonia Production ◽  
Force System ◽  
Bosch Process

Fast and reliable industrial production of ammonia (NH3) is fundamentally sustaining modern society. Since the early 20th Century, NH3 has been synthesized via the Haber–Bosch process, running at conditions of around 350–500°C and 100–200 times atmospheric pressure (15–20 MPa). Industrial ammonia production is currently the most energy-demanding chemical process worldwide and contributes up to 3% to the global carbon dioxide emissions. Therefore, the development of more energy-efficient pathways for ammonia production is an attractive proposition. Over the past 20 years, scientists have imagined the possibility of developing a milder synthesis of ammonia by mimicking the nitrogenase enzyme, which fixes nitrogen from the air at ambient temperatures and pressures to feed leguminous plants. To do this, we propose the use of highly reconfigurable molecular metal oxides or polyoxometalates (POMs). Our proposal is an informed design of the polyoxometalate after exploring the catabolic pathways that cyanobacteria use to fix N2 in nature, which are a different route than the one followed by the Haber–Bosch process. Meanwhile, the industrial process is a “brute force” system towards breaking the triple bond N-N, needing high pressure and high temperature to increase the rate of reaction, nature first links the protons to the N2 to later easier breaking of the triple bond at environmental temperature and pressure. Computational chemistry data on the stability of different polyoxometalates will guide us to decide the best design for a catalyst. Testing different functionalized molecular metal oxides as ammonia catalysts laboratory conditions will allow for a sustainable reactor design of small-scale production.

Ultrasonic Sonic Imaging for a Two Phase System Based on Support Vector Machine Classifier

2014 ◽  
Vol 1016 ◽  
pp. 273-278
Author(s):  
Mohd Faizal Mat Desa ◽  
Muhammad Naufal Mansor ◽  
Ahmad Kadri Junoh ◽  
Amran Ahmed ◽  
Wan Suhana Wan Daud ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Lamb Waves ◽  
Industrial Process ◽  
Observation Time ◽  
Support Vector ◽  
Phase System ◽  
Ultrasonic Tomography ◽  
Two Phase ◽  
Horizontal Pipe ◽  
Process Tomography

Multiphase flow characterization is an important task for monitoring, measuring or controlling industrial processes. This can be done by means of process tomography. The use of tomographic techniques has been used within the oil industry. One of the potential applications is flow visualization and measurement in producing wells. Research on industrial process tomography consists in obtaining estimated images of a cross section of a pipe or vessel containing or carrying the substances of the process. One category of process tomography is ultrasonic tomography technique. A simple tomography can be built by mounting a number of sensors around the circumference of a horizontal pipe. This includes acquiring and processing ultrasonic signals from the transducers to obtain the information of the spatial distributions of liquid and gas in an experimental column. Analysis on the transducers’ signals will be carrying out to distinguish between the observation time and the Lamb waves. The information obtained from the observation time is useful for further development of the image reconstruction. To obtain the time easily, the time will be calculated from the starting pulse of transmitter signal until the starting peak of receiver signal. Finally Support Vector Machine (SVM) was employed to distinguish of each phase between water and gas.

On Small Scale LNG Concepts

2021 ◽  
Author(s):  
Rainer Kurz ◽  
Min Ji ◽  
Griffin Beck ◽  
Timothy C. Allison
Keyword(s):  
Gas Turbines ◽  
Operating Conditions ◽  
Process Efficiency ◽  
Small Scale ◽  
Control Methods ◽  
Electric Drives ◽  
Output Driver ◽  
The Relationship ◽  
Equipment Performance

Abstract The different economics of small scale LNG plants put more emphasis on capital expenses over process efficiency, and thus favors simpler refrigeration cycles. We typically find reverse Brayton cycles, or SMR (Single mixed refrigerant) cycles. These cycles have specific requirements to the compression equipment, and typically have smaller drivers, either electric drives or gas turbines. The relationship between output, driver size, and process preferences is explained. The type of compressors, and expanders needed are discussed, together with thoughts and the driver preferences. This includes the different control methods that can be used, both for the cycle adaptation, as well as the related control of the compressors, expanders, valves and drivers. Equipment performance maps are created to highlight the required different operating conditions. This result allows for subsequent optimization discussions.

scholarly journals Successful Small-Scale Household Relocation after a Millennial Flood Event in Simbach, Germany 2016

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 156 ◽  
Author(s):  
Barbara Mayr ◽  
Thomas Thaler ◽  
Johannes Hübl
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Planning Process ◽  
Flood Risk Management ◽  
Flood Event ◽  
Small Scale ◽  
Successful Implementation ◽  
Adaptive Planning ◽  
Extreme Flood ◽  
Household Relocation

International and national laws promote stakeholder collaboration and the inclusion of the community in flood risk management (FRM). Currently, relocation as a mitigation strategy against river floods in Central Europe is rarely applied. FRM needs sufficient preparation and engagement for successful implementation of household relocation. This case study deals with the extreme flood event in June 2016 at the Simbach torrent in Bavaria (Germany). The focus lies on the planning process of structural flood defense measures and the small-scale relocation of 11 households. The adaptive planning process started right after the damaging event and was executed in collaboration with authorities and stakeholders of various levels and disciplines while at the same time including the local citizens. Residents were informed early, and personal communication, as well as trust in actors, enhanced the acceptance of decisions. Although technical knowledge was shared and concerns discussed, resident participation in the planning process was restricted. However, the given pre-conditions were found beneficial. In addition, a compensation payment contributed to a successful process. Thus, the study illustrates a positive image of the implementation of the alleviation scheme. Furthermore, preliminary planning activities and precautionary behavior (e.g., natural hazard insurance) were noted as significant factors to enable effective integrated flood risk management (IFRM).

scholarly journals An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

2018 ◽  
Vol 8 (12) ◽  
pp. 2574 ◽  
Author(s):  
Qinghua Mao ◽  
Hongwei Ma ◽  
Xuhui Zhang ◽  
Guangming Zhang
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Pso Algorithm ◽  
Conveyor Belt ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Steel Cord

Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

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