Process Control Decisions Research Articles

Onboard guidance and control algorithms for increased autonomous aerobraking capabilities at Mars

Aerobraking represents a valuable option for interplanetary missions thanks to its capability of reducing the orbital semi-major axis through multiple atmospheric passages, thus with limited fuel consumption. However, due to structural and thermal loads the spacecraft undergoes, mission risks and ground operations costs increase. This study aims to tackle this aerobraking drawback by enhancing its autonomy. The main activities required for aerobraking execution, including atmospheric prediction, orbital state estimation, and orbit control, are tailored for onboard execution. Regarding orbit control, two different methodologies, differentiated by working principle and prediction horizon breadth, are investigated. The first plans orbit control manoeuvres based on the prediction of the atmospheric conditions for a single upcoming pericentre, while the second schedules and optimizes the manoeuvres considering the recent atmospheric conditions experienced by the spacecraft and the altitude trend of multiple upcoming pericentres. The whole operational concept is tested within a simulated environment, with the primary objective of accurately reproducing the intense atmospheric variations of the Martian atmosphere, which represents the most challenging aspect during aerobraking execution. The numerical testing, involving the simulation of different aerobraking regimes under varied conditions, shows that the natural dynamics of the orbital motion can be exploited to minimize both fuel consumption for orbit control manoeuvres and spacecraft load variations, thus enabling more efficient aerobraking and more stable flight conditions, even with the limited predictive capabilities that characterize onboard resources. Both control logics permit the aerobraking execution without exceeding the maximum allowed thermal limits, hence showing their effectiveness. Furthermore, a Monte Carlo analysis conducted on a complete aerobraking simulation reveals a 25.5% reduction in propellant consumption and a 9.4% reduction in heat rate variability when the orbit control decision process uses information relative to multiple future pericentres.

Multi-Criteria Optimization Conditions for the Recovery of Bioactive Compounds from Levisticum officinale WDJ Koch Roots Using Green and Sustainable Ultrasound-Assisted Extraction

Given that ultrasound-assisted aqueous extraction is gaining importance within “green technology” and to increase the efficiency of extracting bioactive compounds from Levisticum officinale root waste, optimization of its parameters was undertaken. Multi-objective (multi-criteria) optimization can be an extremely promising tool not only for designing and analyzing the extraction process, but also for making process-control decisions. Therefore, the main objective of this study was to develop and optimize an environmentally friendly ultrasound-assisted extraction methodology for the aqueous extraction of bioactive compounds from the roots of Levisticum officinale, which are considered a by-product. The focus was on determining the optimal extraction conditions of the independent variables, such as solid–liquid ratio, extraction time and ultrasound power, so that the optimized extracts present the highest bioactive potential expressed in terms of levels of phenolic compounds, flavonoids, sugars and antioxidant potential. Based on the Pareto-optimal solution sets, it was found that to maximize the criteria, aqueous extraction should be carried out at a Levisticum officinale biomass/solvent ratio of 0.0643 g/mL for a time of 8.1429 to 9.0000 min, with ultrasound assistance of 162.8571 to 201.4286 W. Among the compromise solutions, the so-called “best efficient solution” was indicated as the solution for which the Euclidean distance from the ideal point of Utopia was the smallest (among all analyzed points of the collection), which had coordinates x1comp = 0.0750 g/mL, x2comp = 9.0000 min and x3comp = 214.2857 W. The results obtained will provide a valuable tool to assist in the decision-making process of controlling such an extraction process.

Happiest streaming platform on earth: The role of presumed influence in the parental control decision process

In accordance with the influence of presumed influence (IPI) model, parents’ views toward utilizing parental controls on video streaming platforms should be related to perceptions of content’s negative influence on children. Perceptions of child-friendliness of streaming platforms and family cohesion levels should also be expected to mediate and moderate, respectively, this relationship such that perceiving a platform as child-friendly would be negatively related to judgments of content’s negative effects on children as well as views toward using parental controls. The present survey of parents ( N = 806) generally revealed that parents believing content of specific ratings would negatively affect their child expressed greater likelihood of using parental controls on Netflix, Amazon Prime Video, and Disney+. In addition, this relationship was mediated by perceptions of platform child-friendliness but was not moderated at the second stage by family cohesion. Theoretical and practical implications are discussed.

Evaluating cognitive framework of slab yard control decision process by participatory serious gaming simulation

Evaluating cognitive framework of slab yard control decision process by participatory serious gaming simulation

Combining neural networks and control: potentialities, patterns and perspectives

Machine learning tools are widely used for knowledge extraction, modeling, and decision tasks; a range of problems that Control Theory also tackles. Their relations have been largely explored by looking at stochastic control and Markov Decision Processes, due to the proximity of their formulations. However, novel links between machine learning and deterministic control are emerging; combining both approaches, e.g. by performing identification with learning, or controlling the training process. The recent flourishing literature is vast: there is a need to identify challenges, trends and opportunities on this interface. This survey contributes i) to the compared analysis of both fields. ii) Based on literature review, a categorization of combinations of learning and control is drawn. In the control framework, learning has been used for modeling, controllers tuning or adaptation, generating a controller or as a controller itself, for translating complex objectives, or checking controlled systems. Conversely, in the learning framework, control is used for tuning hyperparameters, selecting or generating training data, as the training or decision-making algorithm itself or to guarantee learning properties. iii) Finally, discussions on the literature open novel promising combinations to be explored, such as control of neural networks’ training process.

Process analytical technologies for precise, timely and representative elemental and moisture measurement for conveyed flows

Industrial and technological processes are very difficult to manage when the quality of feed and product or discard are not measured with confidence. Effective control can occur when process analytical technologies are chosen that provide representative, precise, and timely measurements. For the measurement technique to be representative it must comply with the Theory of Sampling (TOS) and provide an equal chance of any component in the streaming material to be included in the support for the measurement. This generally precludes technologies that measure only the surface of materials, or biased measurements stemming from a limited portion of the material only, particularly in the minerals processing and recycling sectors, which usually display high compositional variability. The location of the analytical technology should relate to the benefit being targeted and allow for enough reaction time to respond to the quality in some way; diverting short increments based on composition and decision parameters based on process impact, blending with other quality materials, or feeding information backwards or forwards. Feed forward options can include flow rate control, reagent control, operational process variables that impact recoveries, etc. Major benefits have been achieved in measuring coarse conveyed flows with high specification Prompt Gamma Neutron Activation Analysis (PGNAA) over short increments (thirty seconds to two minutes) for most elements, or over five to ten minute increments for trace elements, such as gold. PGNAA applied to conveyed flows allow the full flow to be measured continuously and composition averaged for each increment in real time. The use of penetrative and continuous moisture measurement using transmission microwaves has also proved effective for moisture monitoring and management. Precisions between laboratory samples of the flow and analysis data from analysers can be sufficient to have high confidence in resulting process control decisions. This paper explains the benefits in more detail and includes case studies to highlight actual benefits derived from the application of such systems. It should be noted that sampling of the materials is still required for calibration and adjustments for the process analytical tools.

Improved Models for Chromate Removal Using Ion Exchangers in Drinking Water Applications

Chromates are widely used for their anticorrosive properties. Unfortunately, they are hazardous, with environmental agencies regulating their levels to below 10 parts per billion (ppb) in drinking water. Because single-use anion-exchange resins are the standard tools for chromate removal, accurate predictive dynamic models are necessary for quick decisions based on deviations of the inlet concentration to achieve the desired outlet purity requirements. To this end, we studied various dynamic models to simulate the ion exchange process for targeted chromate removal. Of those studied, we selected the Thomas model for its accuracy. The Thomas model has two parameters, which are estimated via minimization of the sum of square errors between the predicted model and the experimental data set with varying process conditions. In these analyses, the resulting model demonstrated large parameter fluctuations with contact times and inlet chromate concentrations. Therefore, relying on fixed-parameter values can lead to faulty predictions and, therefore, to poor controllability of the chromate removal ion-exchange system. Thus, in this work, we evaluated the variation of parameters with respect to resin contact times and inlet chromate concentrations and performed a detailed sensitivity analysis and confidence region studies. This work improves the accuracy of the modified Thomas model fivefold and demonstrates its potential for use in process control decisions.

False Data Injection Detection in Cyber-Physical System

The interconnectedness of the CPS allows sharing data with relevant systems and taking more informed process control decisions; however, it also broadens the attack surface. One pain point of IoT, technology usually applied in CPS, is that embedded devices are usually low energy and do not have enough processing power to operate while keeping up with security measures, such as encryption and data validation. As a result, attackers may use such devices to launch attacks on the network, compromising the whole CPS infrastructure. One common type of attack is the False Data Injection (FDI), in which the attacker has access to a communication channel and can change the value read by a sensor or sent to an actuator. One way of coping with such attacks is to develop a bank of observer by using Functional Observers, which are better suited for high-dimensional, sparse systems. It does not suffer from sparsity\’s numerical problems and uses a reduced-order system to observe only the desired states, significantly reducing the computational burden of the observer. We propose an LMI-based approach to design a bank of residual generators for functional observers to detect such attacks. This approach has the advantage of using a reduced order arbitrary dynamic system, making it suitable for large-scale smart grids, and the use of LMI, allowing the easy insertion of restrictions

Um sistema de controle de processos cibernéticos com base em reconhecimento de padrões e computação em nuvem

Abstract: This paper presents a novel simulation model of the Cyber Process Control System (CPCS) by combining pattern recognition and Cloud Computing (CC). This paper's originality arises from its aim to build a cloud computing platform for autonomous machines, and the exploration of manufacturing data to generate interpretable patterns to be used in process control decision making. The combining of Cloud technology and machine learning brings production to Industry 4.0. The proposed system is tested using data Carbon Fiber Reinforced Polymer (CFRP) routing process. The little information available about the manufacturing process of this type of material and the interaction between the production steps makes the manufacturing process quite difficult. This system generates interpretable rules of controllable operating parameters sent to the controller to keep the machining process within the limits of the specifications. The second step is activated during the drifting conditions in the machining step. Also, the simulation of the machining process is illustrated to generate the relations between input and output variables of the machining process. The findings of the corrective actions are illustrated and the interaction between the two industrial steps is simulated. Finally, current and future CPCS and CC applications in Industry 4.0 are discussed.

Distributionally robust Optimal Control and MDP modeling

In this paper we discuss Optimal Control and Markov Decision Process (MDP) formulations of multistage optimization problems when the involved probability distributions are not known exactly, but rather are assumed to belong to specified ambiguity families. The aim of this paper is to clarify a connection between such distributionally robust approaches to multistage stochastic optimization.

DASC: Towards a road Damage-Aware Social-media-driven Car sensing framework for disaster response applications

While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more pervasive, but one of its key limitations lies in its inconsistent reliability stemming from the data contributed by unreliable human sensors. In this paper, we present DASC, a road Damage-Aware Social-media-driven Car sensing framework that exploits the collective power of social sensing and VSNs for reliable disaster response applications. However, integrating VSNs with social sensing introduces a new set of challenges: (i) How to leverage noisy and unreliable social signals to route the vehicles to accurate regions of interest? (ii) How to tackle the inconsistent availability (e.g., churns) caused by car drivers being rational actors? (iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media? The DASC framework addresses the above challenges by establishing a novel hybrid social-car sensing system that employs techniques from game theory, feedback control, and Markov Decision Process (MDP). In particular, DASC distills signals emitted from social media and discovers the road damages to effectively drive cars to target areas for verifying emergency events. We implement and evaluate DASC in a reputed vehicle simulator that can emulate real-world disaster response scenarios. The results of a real-world application demonstrate the superiority of DASC over current VSNs-based solutions in detection accuracy and efficiency.

Universal reliance on LAL/TAL in bacterial endotoxin detection in drugs and parenteral - An ecological and economic perspective


 Introduction: Endotoxin detection and quantification in biological drugs and parenteral is a crucial step in helping us to be confident in process control (IPC) and product release decisions. Food and Drug Administration (FDA) approves either Limulus Amebocyte Lysate (LAL) /Tachypleus Amebocyte Lysate (TAL) derived from blue blood of horseshoe crabs to detect and quantify endotoxin. This in turn increases harvest pressure on wild crabs for biomedical bleeding practice which eventually leads to post bleeding mortality up to 30%. In economic perspective, the LAL/TAL industry worth more than 50 million USD annually. Methods: Numbers of methods were adopted to enhance the accuracy of endotoxin detection, including gel clot, turbidimetric and chromogenic method. Results: However, all these methods require crabs either from wild or from captive for bleeding purpose. Though, various conservative measures are being taken, the habitat degradation and anthropogenic pressures on wild crab population is still beyond control and leads to unsustainable utilization of natural stock. Conclusions: Due to an increased demand of LAL/TAL, the present review paper addresses the issues related to Eco-economical viewpoint on sustainable utilization of wild stock for future LAL/TAL industry. It also addresses pros and cons of recent advanced sensor technology in rapid and low cost detection of endotoxin.

ПОБУДОВА ФУНКЦІОНАЛЬНОЇ МОДЕЛІ АВТОСЕРВІСНОГО ПІДПРИЄМСТВА

This article analyzes the latest trends in the transport industry and information technology. Functional model of auto-service enterprise (ASE) has been constructed, which together with its parametric and morphological description gives an opportunity to evaluate internal and external factors that affect the quality of services performed and to identify options for improving the properties of the enterprise. The process of ASE functioning was divided into 8 levels, each of which has a specific purpose. Levels A, C, E, G are responsible for executing subprocesses and B, D, F, H for improving them. In the main process, the following main subprocesses were identified: “Planning and maintenance of the activity of the ASE”, “Ensuring the implementation of technological processes”, “Control over the activity of the ASE”, “Optimization of the activity of the ASE on the basis of the intellectual system”. In addition, for all subprocesses the moments of bilateral influence of the environment and the processes of operation of the ASE are defined. The connection between these subprocesses and the external environment is provided by external and internal energy, mass and information flows that form the database of input and output data. Each subprocess can be improved by correcting the data after analyzing the constraints. Improvement of subprocesses is based on optimization methods and models, including models of inventory management with limited storage capacity. The use of intelligent process control systems and decision support systems is envisaged. On the basis of the proposed functional model and its description, it becomes possible to carry out a mathematical description of ASE main operation subprocesses, to develop algorithms of software modules for automated control over the production process of car services and their improvement, which will be included in the integrated quality management system. KEYWORDS: AUTOSERVICE ENTERPRISE, FUNCTIONAL MODEL, PRODUCTION PROCESS, QUALITY

An integrated model of production scheduling, maintenance and quality for a single machine

It is well known that production scheduling, maintenance and process control decisions affect each other. Hence it is both essential and prudent to optimize them jointly. This paper develops a model that integrates and optimizes production, maintenance and process control decisions simultaneously for a single machine. Both types of maintenance, preventive and corrective, are taken into consideration. The methodology starts by developing an optimal preventive maintenance schedule. Then an integrated model that determines the decision variables and optimizes the total cost per unit time, resulting from production scheduling, inventory holding and maintenance and process control is developed. The utility of the model is demonstrated using an example from the literature. Systematic sensitivity analysis demonstrated that the model results are robust. Also; the model results are compared with the results of optimizing each system separately and with two-way integration. The comparisons indicate that the developed model results in a saving that ranged from 2.62 to 6.78 percent. The model provides a viable approach for optimizing production scheduling, maintenance and quality jointly.

Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning

A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawing process. The control model is built during consecutive process executions under optimal control via reinforcement learning, using the measured product quality as reward after each process execution. Prior model formulation, which is required by state-of-the-art algorithms from model predictive control and approximate dynamic programming, is therefore obsolete. This avoids several difficulties namely in system identification, accurate modelling, and runtime complexity, that arise when dealing with processes subject to nonlinear dynamics and stochastic influences. Instead of using pre-created process and observation models, value function-based reinforcement learning algorithms build functions of expected future reward, which are used to derive optimal process control decisions. The expectation functions are learned online, by interacting with the process. The proposed algorithm takes stochastic variations of the process conditions into account and is able to cope with partial observability. A Q-learning-based method for adaptive optimal control of partially observable episodic fixed-horizon manufacturing processes is developed and studied. The resulting algorithm is instantiated and evaluated by applying it to a simulated stochastic optimal control problem in metal sheet deep drawing.

Process Control Decision Inference, Monitoring, and Execution

Approaches for inferring process control system decision trees from plant data have been heavily researched and demonstrated, but the utility of applying such decision tree inferences for autonomously monitoring, guiding, and executing control philosophies has been lacking. The authors have implemented an architecture leveraging model-based reasoning and graphical programming that investigates the utility of using control decision tree inferencing for validating, monitoring, and executing control strategies for both simple and complex process control problems. The techniques are potentially useful for assessing process control health, as well as extracting process control knowledge that may exist in daily operations but not recognized or well understood by analysts and management. Plant operators and managers can readily employ such insights for improving, augmenting, and extending process control system behavior, but can also potentially employ the refined and validated decision trees as a supervisory control layer on top of their existing control systems.

Reinforcement Learning – Overview of recent progress and implications for process control

This paper provides an introduction to Reinforcement Learning (RL) technology, summarizes recent developments in this area, and discusses their potential implications for the field of process control, and more generally, of operational decision-making. The paper begins with an introduction to RL that allows an agent to learn, through trial and error, the best way to accomplish a task. We then highlight new developments in RL that have led to the recent wave of applications and media interest. A comparison of the key features of RL and mathematical programming based methods (e.g., model predictive control) is then presented to clarify their similarities and differences. This is followed by an assessment of several ways that RL technology can potentially be used in process control and operational decision applications. A final section summarizes our conclusions and lists directions for future RL research that may improve its relevance for the process systems engineering field.

Factors affecting SVI in small scale WWTPs

This paper analyses factors associated with bulking in 195 small scale wastewater treatment plants (WWTPs) in Estonia. Operational data from each plant were collected and analysed statistically. The key factors associated with bulking were infiltration into sewage pipes, the type and purpose of process reactor, operational practices and influent characteristics. Both anaerobic fraction and volumetric fraction of the anaerobic reactor compared to the aerobic reactor resulted in a positive correlation with sludge volume index (SVI) <150 ml/g values. Good operation and maintenance practice as well as an operator's competence play a crucial role in bulking prevention. Using the 30 minute settling test (V30) as the single process control parameter can mislead an operator's judgement in process control strategies and cause effluent violations. Misjudgements in process control decisions can lead to unwanted conditions in small WWTPs (e.g. excessive chemical addition favoured bulking). Use of instrumentation, control and automation helped to keep the process conditions more stable and reduce the probability of bulking. Analyses of variance showed that the factors associated with Microthrix parvicella growth were long solids retention time (SRT), low food-to-microorganism ratio (F/M) and lack of carbon content compared against nitrogen and phosphorus contents.

Application of fuzzy theory for identifying the required availability of an autonomous localization unit in European Train Control System

According to the evolution tendency of the control decision process from a trackside to a train-borne system, various autonomous localization units for railway vehicles were developed. As recommended in railway standards, the design process of each system, here the autonomous localization units (LU), follows the V-model whose first step is to define its availability requirement in order to satisfy the global ETCS system requirements. The classical approach for assigning the subsystem availability is based on the assumption that failure parameters of other units are precisely known. This assumption is too restricted in reality due to the lack of information. In this paper, we propose a new approach that allows taking into account uncertainties in the dependability parameters of the ETCS components for identifying the upper threshold of the LU unavailability to reach ETCS availability requirements. Using fuzzy fault trees, the fuzzy unavailability of the ETCS without the autonomous LU is evaluated. Then, based on its membership function, we assess the satisfaction rate that an advanced ETCS with the autonomous LU can satisfy the ETCS availability target.

G-SIR: An Insider Attack Resilient Geo-Social Access Control Framework

Insider attacks are among the most dangerous and costly attacks to organizations. These attacks are carried out by individuals who are legitimately authorized to access the system. Preventing insider attacks is a daunting task. The recent proliferation of social media and mobile devices offer new opportunities to collect geo-social information that can help in detecting and deterring insider attacks. In particular, such geo-social information allows us to better understand the context and behavior of users. In this paper, we propose a Geo-Social Insider Threat Resilient Access Control Framework (G-SIR) to deter insider threats by including current and historic geo-social information as part of the access control decision process. We include policy constraints to manage the risks of colluding communities, proximity threats, and suspicious users while leveraging the presence of users around the requester to make an access decision. By examining users’ geo-social behavior, we can detect those users whose access behavior deviates from the expected patterns; such suspicious behaviors can point to potential insider attackers who may deliberately or inadvertently carry out malicious activities. We use such information to establish how trustworthy a user is before granting access. We evaluate the G-SIR framework through extensive simulations and our results show that the proposed approach is efficient, scalable and effective.