Level Of Process Control Research Articles

A novel robust Virtual Reference Feedback Tuning approach for minimum and non-minimum phase systems

In real-world applications, it is often desired that the design of a closed-loop system must attain not only high performance but also robustness. This paper presents a novel robustness-based formulation for control for discrete time minimum and non-minimum phase systems using the Virtual Reference Feedback Tuning (VRFT) framework. The proposed idea is to design robust lower and higher order Proportional Integral and Derivative (PID) controller using Maximum Sensitivity (Ms) based closed-loop reference model by minimization of the VRFT objective function (JVR) The efficacy of the approach is verified on various systems in simulation and also demonstrated on the Temperature Control Process and Level Control Process experimental setups. The proposed VRFT approach provides improved performance and robustness for set point tracking and disturbance rejection. Also, the proposed approach ensures stability and specific robustness of the closed-loop system. Further, the fragility of the controller is investigated for perturbations in the controller parameters.

Methodology for Determining the Level of Process Control in Complex Systems Taking into Account Risk-oriented Factors From Safe Time to Pandemics

Research has been conducted on the current problem of developing a methodology for determining the level of process control in complex systems, taking into account risk-oriented factors influencing it. For the first time, the study proposes stages of risk management in the process of assessing the level of controllability of complex systems, formalizes the input data used to assess risks using fuzzy models for various complex systems, constructs generalized step-by-step algorithms for risk-oriented assessment of controllability in complex systems. An experimental approbation has been made of the research on the problem of determining the level of process control in the airline system, taking into account risk-oriented factors influencing it. The study will be a useful tool to support decision-making to improve process control in various complex systems by taking into account the risks and threats to its operation for managers from safe time to pandemics as COVID-19. © 2021 Copyright for this paper by its authors.

Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel

Directed energy deposition (DED) is an emerging technology with repair applications in critical aerospace components. Mechanical properties of DED components have been shown to vary significantly through a part, making it difficult to achieve the level of process control required for these applications. Using thermal data captured in-situ, cooling rates and melt pool dimensions were calculated and related to the final grain structure, captured by EBSD. The changes in cooling rate explain the microstructural variation between different processing parameters and through the build height. A new approach, using a cumulative anisotropy factor was implemented and correlates the variation in hardness with grain structure. Two regimes were found depending on the linear heat input in 316L, with high linear heat input resulting in great amounts of mechanical anisotropy on the component level. The relationships between thermal signature and mechanical properties suggest close control of anisotropy could be achieved by monitoring and controlling the melt pool size using a coaxial camera. • Walls built with high heat input shown to have significantly increased grain structure anisotropy. • Grain structure explained through cooling rate and melt pool size, varying with both processing parameters and build height. • Hardness shown to be directly related to grain structure, suggesting it can be controlled using the thermal signature.

Modeling and Validation of a Four‐Tank System for Level Control Process Using Black Box and White Box Model Approaches

Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four‐tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First‐Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude‐modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A lifecycle operating performance assessment framework for hot strip mill process based on robust kernel canonical variable analysis

In the modern hot strip mill process (HSMP), the operating performance may deteriorate because of wear of equipment, mode transitions, and random disturbances. If the process is not adjusted and maintained, faults may occur, resulting in greater economic losses and potential safety hazards. Therefore, it is of great practical significance to carry out comprehensive operating performance assessment. In this paper, a lifecycle operating performance assessment framework based on robust kernel canonical variable analysis (RKCVA) is proposed to deal with automation hierarchy, nonlinearity, and outliers in the plant-wide HSMP. First, the HSMP is divided into upstream, midstream, and downstream in real-time control level (L1). Then, based on kernel canonical variable analysis (KCVA) and partial robust M-regression (PRM), the RKCVA models are developed for each stream and process control level (L2). Based on the Bayesian inference, statistical fusion is implemented to judge whether the process is in normal or faulty operating condition. After that, according to different evaluation rules of different operating conditions, the lifecycle operating performance assessment is realized. Finally, the framework is illustrated with a case study on a real HSMP. The assessment results show that the accuracy of the RKCVA is more than 10% higher than that of the KCVA.

Pseudomonas mendocina LYX: A novel aerobic bacterium with advantage of removing nitrate high effectively by assimilation and dissimilation simultaneously

The problem of nitrate accumulation in aerobic tank and total nitrogen excessive discharge in effluent was very common in traditional livestock and poultry farming wastewater treatment systems owing to the lengthy process flow and low process control level. A strain LYX of aerobic bacterium was isolated from the activated sludge of a wastewater treatment system in a pig farm, which could remove nitrate effectively in aerobic tank and was identified Pseudomonas mendocina by 16S rRNA sequencing. Under the condition of nitrate as the sole nitrogen source, this strain removed over 90% of NO3−-N with an initial concentration of 110 mg/L under aerobic conditions within 48 hours. Among them, 37.9% of NO3− -N was assimilated into Bio-N, about 51.9% was reduced to gaseous nitrogen and less than 0.5% of nitrogen was replaced by NO3−-N and NH4+-N, 9.7% NO3−-N remained in the effluent at the end. At the same time, four key genes (napA, nirK, norB and nosZ) related to nitrate nitrogen removal were expressed during the denitrification process of P. mendocina LYX, in which the transcription level of the indicator genes of this aerobic denitrifying bacterium (napA) was the highest. In addition, it was found with the 15N tracer technique that inoculation of this strain on sludge increased the amount of nitrogen loss from 9.26 nmol N/(g·h) to 23.835 nmol N/(g·h). Therefore, P. medocina LYX is a potential bioagent for advanced nitrogen removal by assimilating and reducing nitrate simultaneously in aerobic tanks.

Eco-Mimicry Opens New Doors for Bioprocess Engineers

The natural world blends microenvironments to create a continuum of spatial niches wherein microbial consortia participate in metabolic exchanges. The capability with which nature selects a consortium to complement a given niche is often taken for granted. Not by Shahab et al. (2020), however, who created a synthetic microbial consortium to mediate a multi-step bioprocess in a single bioreactor system. The concept affords a fair degree of modularity in the choice of biocatalyst and a reasonable level of process control, thus making it an engineering tool.

Energy-aware production scheduling for additive manufacturing

In recent years, additive manufacturing (AM), also popularly known as three-dimensional (3D) printing, has received significant attention as an emerging manufacturing technology in various industrial sectors, e.g., automotive, aerospace, and medical devices, due to its unique advantages compared to other conventional manufacturing techniques. As tied to the active application of AM to real-world practice, there have been significant efforts to improve energy efficiency and reduce energy cost in AM. Given this context, this study proposes a systematic approach that is uniquely designed to achieve energy-aware production scheduling for AM while it addresses both process-level and scheduling-level controls in an integrated fashion to ultimately save electricity cost. In particular for the process-level control, we first analyze and extract power demand patterns paired with the various process parameter settings. For the scheduling-level control, we formulate a production scheduling problem to minimize energy cost in response to time-varying electricity price as well as demand charge, which has been a common practice for calculating the electric bills of industrial consumers. Specifically, to realize energy-aware production scheduling, the selection of process parameters will be considered as a part of scheduling decisions so that peak power demand and energy consumption patterns can be flexibly adjusted by changing the process parameter setting. While considering the fused deposition modeling (FDM) as a fabrication method, this study conducts numerical experiments based on real-world data to demonstrate the improvement by the proposed approach.

Automation system for oil and gas service enterprises

The paper presents the results of the development of a single integrated solution for the automation of technological processes of service enterprises of the oil and gas sector, which allows to organize the work of production and management in a single information space. Algorithms have been developed to solve individual problems that are interconnected into certain algorithmic modules. The results of the integration analysis of modern automation systems of the ERP-class are presented, in particular, the level of adaptation to the stages of the product life cycle, compliance with standards and safety requirements, the complexity of the transition to the automation system. The results of the analysis of possible risks of the project during the implementation of this algorithm are presented. The practical significance of the development of a single production management algorithm is described. Finally, the treatise evaluates the main advantages and economic effect of the introduction of an automation system for an enterprise engaged in the production of oil and gas pipes at the process control level.

Microbial Dynamics in Traditional and Modern Sour Beer Production.

Traditional sour beers are produced by spontaneous fermentations involving numerous yeast and bacterial species. One of the traits that separates sour beers from ales and lagers is the high concentration of organic acids such as lactic acid and acetic acid, which results in reduced pH and increased acidic taste. Several challenges complicate the production of sour beers through traditional methods. These include poor process control, lack of consistency in product quality, and lengthy fermentation times. This review summarizes the methods for traditional sour beer production with a focus on the use of lactobacilli to generate this beverage. In addition, the review describes the use of selected pure cultures of microorganisms with desirable properties in conjunction with careful application of processing steps. Together, this facilitates the production of sour beer with a higher level of process control and more rapid fermentation compared to traditional methods.

Properties of Laser Additive Deposited Metallic Powder of Inconel 625

Abstract Paper presents results of laser additive manufacturing. Deposition of nickel based super alloy Inconel 625 was performed. Laser metal deposition is advanced manufacturing process dedicated for prototyping and low scale series production. Inconel 625 is nickel based super alloy, with high heat resistance properties. Therefore due material properties and chemical composition is characterized as a difficult to machining [1, 2]. Additive manufacturing process using focused photons beam for selective deposition of metallic powder in laser engineered net shaping (LENS) method can be used as alternative technology. High energy density of controllable laser beam combining with coaxial delivery system allow to precise deposited metallic powder. Manufacturing process are based on selective melting of additional material using laser radiation and crystallization process. An additional material in form of filler wire as well as metallic powder can be used. Advantages of using metallic powder are higher level of process control, nevertheless adequate selection of process parameters are required. High energy density of laser beam and rapid crystallization process affect on metallographic structure of deposited material. Thermal energy absorbed in material affect on phase transformation.Molten powder mixing with base material changing metallographic structure. Chemical composition of obtained overlay weld are combination of base and additive material. Therefore to achieve stable crystallization process chemical composition of additive material wassimilar to base material. Additional alloying elements could affect on mechanical properties. Deposition process using TruLaserCell 1005 laser machine was performed. To determine properties of manufactured material metallographic analysis and destructive tests were performed.

Control of passivation and compensation in Mg-doped GaN by defect quasi Fermi level control

A defect quasi Fermi level (dQFL) control process based on above bandgap illumination was applied to control H and VN-complexes, which are the main contributors to the passivation and self-compensation, respectively, in Mg:GaN grown via metalorganic chemical vapor deposition. Secondary ion mass spectrometry measurements confirmed that the total Mg incorporation was unaffected by the process. However, the total H concentration was reduced to similar levels obtained by post-growth thermal activation prior to any annealing treatment. Similarly, the 2.8 eV emission in the photoluminescence spectra, attributed to compensating VN and its complexes, was reduced for the dQFL-process samples. After thermal activation and Ni/Au contact deposition, Hall effect measurements revealed lower resistivities (increased mobilities and free hole concentrations) for dQFL-grown samples with Mg doping concentrations above and below 2 × 1019 cm−3. All these results demonstrate that the dQFL process can effectively reduce the H-passivation and self-compensation of the Mg:GaN films.

A Novel Method And Implementation Of Digital Algorithm For First Order Non Linear Spherical Tank Level Control Process With An Integral Windup

The primary goal of the process control is to control, monitor and to maintain different kinds of process stations at the desired stable conditions for improving the productivity and product quality. In industries most of the process are non - linear, controlling such type of system is difficult. So, spherical tanks are one among the non-linear systems which can be used to store maximum quantity of product in minimum area. These types of non-linear systems violate the superposition principle and are very difficult to control and monitor. Generally, conventional PID Controller is used for controlling a process in a plant. In Level control process, there exists the sudden change in final control element which leads to maximum overshoot and larger settling time, this stated phenomenon requires tuning of gain values to control the process variable to the set point. In order to overcome the above-mentioned problem, Anti reset windup concept is preferred. The mathematical modelling of a given process is obtained using black box system identification methodology. The accuracy in controlling the process variable is complicated due to its dynamic behaviour and characteristics. So in order to maintain the proper characteristics of non-linear system the mathematical modelling has been obtained by conducting the open loop test. In ancient stage the Ziegler Nicholas method is implemented to tune the PID parameters such as Kp, τi and τd which takes larger settling time, maximum Integral Square error and maximum peak overshoot. After tuning the PID gain values with the help of anti reset windup, the overshoot and settling time will be reduced. In this proposed work, Deadbeat and Dahlin’s Algorithms are used to convert the transfer function of the level process from ‘S’ domain to ‘Z’ domain (digital controller). The transfer function is used in MATLAB (Matrix Laboratory) to analyse the output of level process and the simulations are taken for the plant with above mentioned two different algorithms. For real time interface the LabVIEW platform is used and the results are discussed. This proposed digital controller with anti reset windup provides lower steady state error, minimum peak overshoot and minimum error criteria indices.

Mathematical Modeling and Quantitative Feedback design for Industrial Nonlinear Storage Tank Level Control (NSTLC) Process

Mathematical Modeling and Quantitative Feedback design for Industrial Nonlinear Storage Tank Level Control (NSTLC) Process

Performance Comparison of Passive Fault Tolerant Control Strategy with PI and Fuzzy Control of Single-Tank Level Process with Sensor and System Fault

Fault-Tolerant Control (FTC) strategy has gain maximum attention in recent years in chemical industries due to economical and safety hazards perspective. Targeting at the decreasing control performance of the single-tank level control process under the constraint of system and sensor faults, this article presents model-based Passive Fault-Tolerant Control (PFTC) strategy which are based on conventional and artificial intelligence control. The deviation between system outputs and model output are called residuals and are used to detect and identify faults. The mathematical model of single-tank level system is derived from real time process data using process reaction curve method. The paper discusses about the performance comparison between model-based PFTC using fuzzy logic and conventional proportional Plus Integral controller (PI). The proposed PFTC strategy is applied on single-tank level control process with system and sensor faults and verifies the performance of PFTC using fuzzy logic plus conventional PI control and other PFTC configuration. Proposed PFTC using fuzzy logic plus PI control gives better control performance even though fault occurs in the system. The control performance of different PFTC strategies are measured in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indices.

Analytical Measurements and Efficient Process Generation Using a Dual–Arm Robot Equipped with Electronic Pipettes

The continued growth in life sciences is being accompanied by the constantly rising demand for robotic systems. Today, bioscreening and high–throughput screening processes are well automated. In contrast, a deficit can be found in the area of analytical measurements with complex and frequently changing processes. Robots undertake not only transportation tasks, but also direct sample manipulation and subsequent analytical measurements. Due to their human-like structure, dual-arm robots perform such processes similar to human operation. Liquid handling is required to transfer chemicals, to prepare standard solutions, or to dilute samples. Two electronic pipettes with different volume ranges (5–200 µL and 50–1000 µL) were integrated into a dual–arm robotic system. The main focus in this publication is the software interface for alternating robot and pipette control as well as the high–level process control system. The performance using a dual–arm robot equipped with electronic pipettes and conventional manual pipettes was determined and compared. The automation system presented is the first integration of a dual-arm robot in analytical measurement processes. Conventional manual laboratory pipettes and electronic pipettes are simultaneously used for liquid-handling tasks. The software control system enables a flexible and user-friendly process generation.

Self-Learning Energy Management System on the Process Control Level

An important part of increasing the energy and resource efficiency in companies is the reduction of energy consumption of production plants. In order to achieve this, suitable energy management concepts have to be developed. Energy management concepts involve collecting all required information and making decisions based on the evaluated data. This paper focuses on the approach of shutting down individual plant components in unproductive phases. Because manually shutting down and starting up plants is risky and time-consuming, plants are often left in a state in which they consume a lot of energy, despite not producing any parts, due to both scheduled and unexpected stops. For this reason, adequate energy management concepts are needed that automatically shut down unneeded plant components and restart them in time for the next productive phase. Multiple dependencies between plant components in the context of production and process flow lead to a massive increase in complexity. Subsequently, such concepts are rarely programmed in the control software. In this paper, we provide an approach that implements the energy management concepts as a superordinate entity at the process control level, which enables a holistic plant overview. Using flexible algorithms, the system should be able to make autonomous decisions about the ideal energetic state of the individual plant components. In order to minimize the effort of adding new plants, the developed algorithms should self-adapt to the respective plant configuration autonomously. In addition to machine learning algorithms, the functional analysis of production plants and knowledge concerning the structure of the plants gained from engineering tools are used.

Automated data mining of a plan-check database and example application.

PurposeThe aim of this work was to present the development and example application of an automated data mining software platform that preforms bulk analysis of results and patient data passing through the 3D plan and delivery QA system, Mobius3D.MethodsPython, matlab, and Java were used to create an interface that reads JavaScript Object Notation (JSON) created for every approved Mobius3D pre‐treatment plan‐check. The aforementioned JSON files contain all the information for every pre‐treatment QA check performed by Mobius3D, including all 3D dose, CT, structure set information, as well as all plan information and patient demographics. Two Graphical User Interfaces (GUIs) were created, the first is called Mobius3D‐Database (M3D‐DB) and presents the check results in both filterable tabular and graphical form. These data are presented for all patients and includes mean dose differences, 90% coverage, 3D gamma pass rate percentages, treatment sites, machine, beam energy, Multi‐Leaf Collimator (MLC) mode, treatment planning system (TPS), plan names, approvers, dates and times. Group statistics and statistical process control levels are then calculated based on filter settings. The second GUI, called Mobius3D organ at risk (M3DOAR), analyzes dose‐volume histogram data for all patients and all Organs‐at‐Risk (OAR). The design of the software is such that all treatment parameters and treatment site information are able to be filtered and sorted with the results, plots, and statistics updated.ResultsThe M3D‐DB software can summarize and filter large numbers of plan‐checks from Mobius3D. The M3DOAR software is also able to analyze large amounts of dose‐volume data for patient groups which may prove useful in clinical trials, where OAR doses for large numbers of patients can be compared and correlated. Target DVHs can also be analyzed en mass.ConclusionsThis work demonstrates a method to extract the large amount of treatment data for every patient that is stored by Mobius3D but not easily accessible. With scripting, it is possible to mine this data for research and clinical trials as well as patient and TPS QA.

Soil properties and sediment accretion modulate methane fluxes from restored wetlands.

Wetlands are the largest source of methane (CH4 ) globally, yet our understanding of how process-level controls scale to ecosystem fluxes remains limited. It is particularly uncertain how variable soil properties influence ecosystem CH4 emissions on annual time scales. We measured ecosystem carbon dioxide (CO2 ) and CH4 fluxes by eddy covariance from two wetlands recently restored on peat and alluvium soils within the Sacramento-San Joaquin Delta of California. Annual CH4 fluxes from the alluvium wetland were significantly lower than the peat site for multiple years following restoration, but these differences were not explained by variation in dominant climate drivers or productivity across wetlands. Soil iron (Fe) concentrations were significantly higher in alluvium soils, and alluvium CH4 fluxes were decoupled from plant processes compared with the peat site, as expected when Fe reduction inhibits CH4 production in the rhizosphere. Soil carbon content and CO2 uptake rates did not vary across wetlands and, thus, could also be ruled out as drivers of initial CH4 flux differences. Differences in wetland CH4 fluxes across soil types were transient; alluvium wetland fluxes were similar to peat wetland fluxes 3years after restoration. Changing alluvium CH4 emissions with time could not be explained by an empirical model based on dominant CH4 flux biophysical drivers, suggesting that other factors, not measured by our eddy covariance towers, were responsible for these changes. Recently accreted alluvium soils were less acidic and contained more reduced Fe compared with the pre-restoration parent soils, suggesting that CH4 emissions increased as conditions became more favorable to methanogenesis within wetland sediments. This study suggests that alluvium soil properties, likely Fe content, are capable of inhibiting ecosystem-scale wetland CH4 flux, but these effects appear to be transient without continued input of alluvium to wetland sediments.

Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing.

Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate feedback on process performance. This could help significantly improve cell therapy bioprocessing by allowing proactive decision-making based on real-time process data. Going forward, these types of in-line sensors also open up opportunities to improve bioprocesses further through concepts such as adaptive manufacturing.