Knowledge-based Control System Research Articles

Optimization and validation of the knowledge-based traceability system for quality control in fish waterless live transportation

Waterless live transportation is an effective strategy to realize a larger volume of live fish, and it is pollution-free by contrast with traditional live transport with water. Based on previous study results, this paper aims to propose the knowledge-based HACCP quality control plan to realize real-time feedback and adjustment for fish waterless live transportation. Meantime, the improved traceability system was established to realize the CCPs determination and non-destructive dynamic monitoring of fish stress level in waterless live transportation. Furthermore, to verify the accuracy and applicability of the established traceability system, the relevant manager, workers, and experts are invited to participate in the efficiency and performance analysis. The final results show that the accuracy of the forecasting model is 96.7%, and all the regression coefficients are close to 1.00. Besides, the improved traceability system would offer a better solution of effective quality control for the fish waterless live transportation, and help to improve the transport management efficiency.

The Design Approach of Knowledge-based Management System for Control of Manufacturing Processes of Woven Fabrics

The Design Approach of Knowledge-based Management System for Control of Manufacturing Processes of Woven Fabrics

BUILDING ENERGY SYSTEMS OPERATION OPTIMIZATION WITH ICE STORAGE – A REAL TIME APPROACH

Energy consumption in BESIS is in close relationship with the operation logic of the associated HVAC. Furthermore, engineering calculation norms describe design conditions as the best operating mode for ISAC. Since the performance of ISAC sub-systems are susceptible to environmental variations and operation priorities, controller based dynamic operation optimization of ISAC sub-systems is desirable from both sustainability and business perspectives. This results in an increment in the corresponding heat transfer rate and ultimately, an improvement in the overall efficiency of the BESIS. In this article the assessment results of the dynamic behavior of BESIS in a typical building under certain optimization strategies is presented. Then BEMWIS has been fitted to BESIS to analyze daily operational data of the energy system, to balance load via stored ice latent heat, and to properly respond to the variable inputs. The knowledge based control system uses equipment operation data and energy consumption, developed in form of data bases and mathematical models. The optimization methodology has been developed based of empirical information from the running ISAC. The optimizer methodology predicts load as the result of the decision maker system calculations, and then records, shifts, and eliminates unnecessary energy consumption. Article DOI: https://dx.doi.org/10.20319/mijst.2017.31.2844 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

A knowledge-based control system for the robust manufacturing of deep drawn parts

Throughout a single batch of deep drawing parts the settings of the press have to be adjusted to account for several influences. These can be divided in influences originating through the process, like heating of the tools or aggregation of the lubricant in the tool, and influences originating in the manufacturing of the blanks, like scattering material properties within a coil or between different coils. In the present paper, a method is shown to minimize the effects of both types of influences. The first step in building up a knowledge based control is the quantification of the influences. This is done by running a virtual process tryout based on FEM simulations in order to predict the influence of the scattering material and process properties on the process outcome. For an effective feed forward control based on the variant system, the blank properties are measured during the cutting stage and every part is labeled with a unique identification. The yield strength and ultimate tensile strength are measured by an eddy-current system, while the blank thickness is measured via laser triangulation. As the knowledge of the blank properties alone is not sufficient, a feedback loop is introduced to compensate for the non-blank related influences. For the feedback control, an optical measurement system is proposed, which is able to calculate the draw-in at pre-defined points. The relevant measuring points are defined by evaluation of the correlation between draw-in and changing properties in the virtual process tryout. Both control mechanisms are solely using the usual available and adjustable press settings. In the presented case, the position of the blank as well as the different blankholder forces were chosen. Finally the applicability of the proposed method is evaluated virtually.

Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)

This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controller; (2) Monte Carlo method (trajectory-based random sampling) to find suitable initial values for the control inputs; and (3) optimisation algorithm (performing as a supervisory controller) to re-calibrate these control inputs in order to minimise plant operating costs. The model-based supervisory controller proposed allowed filtration to be optimised with low computational demands (about 5min). Energy savings of up to 25% were achieved when using gas sparging to scour membranes. Downtime for physical cleaning was about 2.4% of operating time. The operating cost of the AnMBR system after implementing the proposed supervisory controller was about €0.045/m3, 53.3% of which were energy costs.

A knowledge-based decision support system for shipboard damage control

The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.

Knowledge-based approach to optimal path generation for an internet-enabled robot

In this paper, a knowledge-based control system for generating an optimal path with obstacle avoidance for an internet-enabled robot is proposed. The novel knowledge-based control system is used for guiding a robot to move from an initial location to a final destination with free collision based on the issued control commands from remote users. In order to address general environment, the obstacles of the robot include static objects and free moving objects. For simplifying the problems, relative distance and velocity have been employed to transform a dynamic object into static one. Simulation results demonstrate effectiveness and feasibility of the proposed knowledge-based control system for the internet-enabled robot.

A knowledge-based control system for air-scour optimisation in membrane bioreactors

Although membrane bioreactors (MBRs) technology is still a growing sector, its progressive implementation all over the world, together with great technical achievements, has allowed it to reach a mature degree, just comparable to other more conventional wastewater treatment technologies. With current energy requirements around 0.6-1.1 kWh/m3 of treated wastewater and investment costs similar to conventional treatment plants, main market niche for MBRs can be areas with very high restrictive discharge limits, where treatment plants have to be compact or where water reuse is necessary. Operational costs are higher than for conventional treatments; consequently there is still a need and possibilities for energy saving and optimisation. This paper presents the development of a knowledge-based decision support system (DSS) for the integrated operation and remote control of the biological and physical (filtration and backwashing or relaxation) processes in MBRs. The core of the DSS is a knowledge-based control module for air-scour consumption automation and energy consumption minimisation.

Fuzzy logic based PWM control and neural controlled-variable estimation of pneumatic artificial muscle actuators

There is increasing research to explore both efficient and cost-effective control of pneumatic artificial muscle (PAM) actuators against inherent nonlinear behaviors of the actuators themselves. This paper presents a fuzzy logic based Pulse-Width-Modulation (PWM) control of PAM actuators together with controlled-variable estimation of a neural network. The PAM actuator consists of two main non-linear elements: a PAM and an on/off control valve unit. Dynamic modeling of the PAM actuator is carried out so as to represent a real PAM actuator in simulation of the dynamic behaviors for gaining knowledge in the controller design. The proportional-type fuzzy control law based on a minimum-time control design is proposed to determine the mass flow rate of compressed air in manipulating the controlled variables of the PAM actuators, such as position and force. In circumstances, when the controlled variables are inaccessible, a neural network model is proposed to estimate those variables instead of using direct measurement. The class of PAM actuators available in industry is used as a practical example to show the effectiveness of the proposed methodology in real working conditions. The concept of the proposed knowledge-based control system, which can emulate the reasoning procedures in this work, can be generalized to systematically implement other PAM actuators in real-time control.

Knowledge-based system for automatic MBR control

MBR technology is currently challenging traditional wastewater treatment systems and is increasingly selected for WWTP upgrading. MBR systems typically are constructed on a smaller footprint, and provide superior treated water quality. However, the main drawback of MBR technology is that the permeability of membranes declines during filtration due to membrane fouling, which for a large part causes the high aeration requirements of an MBR to counteract this fouling phenomenon. Due to the complex and still unknown mechanisms of membrane fouling it is neither possible to describe clearly its development by means of a deterministic model, nor to control it with a purely mathematical law. Consequently the majority of MBR applications are controlled in an "open-loop" way i.e. with predefined and fixed air scour and filtration/relaxation or backwashing cycles, and scheduled inline or offline chemical cleaning as a preventive measure, without taking into account the real needs of membrane cleaning based on its filtration performance. However, existing theoretical and empirical knowledge about potential cause-effect relations between a number of factors (influent characteristics, biomass characteristics and operational conditions) and MBR operation can be used to build a knowledge-based decision support system (KB-DSS) for the automatic control of MBRs. This KB-DSS contains a knowledge-based control module, which, based on real time comparison of the current permeability trend with "reference trends", aims at optimizing the operation and energy costs and decreasing fouling rates. In practice the automatic control system proposed regulates the set points of the key operational variables controlled in MBR systems (permeate flux, relaxation and backwash times, backwash flows and times, aeration flow rates, chemical cleaning frequency, waste sludge flow rate and recycle flow rates) and identifies its optimal value. This paper describes the concepts and the 3-level architecture of the knowledge-based DSS and details the knowledge-based control module. Preliminary results of the application of the control module to regulate the air flow rate of an MBR working with variable flux demonstrates the usefulness of this approach.

KNOWLEDGE-BASED REASONING ENHANCED CONTROL SYSTEM FORIN-SITUBIOREMEDIATION PROCESSES

This study describes the development of a dynamic knowledge-based reasoning-enhanced model predictive control system (KBRECS) for in-situ bioremediation processes. The automated control system balances the complex physical, chemical, and biological processes involved in the remediation process while minimizing overall cost of the entire remediation process. The control system includes an optimization subsystem and a monitoring subsystem. The optimization subsystem consists of a simulation model supported by an optimization function which is designed to generate a series of optimal control actions. The monitoring subsystem is a knowledge-based system which is designed to monitor and adjust the online control actions. The numerical simulation model describes the fate and transport of the subsurface contaminants. The optimization function is a constrained, nonlinear function that has been implemented using a genetic algorithm (GA). Intermediate genetic algorithm individuals are indexed and stored in the knowledge base, thereby reducing search times for values to replace the unqualified schemes used by the monitoring subsystem. The system was applied to a lab experiment and compared with the control system presented in [9]. The results indicated that the knowledge based reasoning system enhanced the control system by generating an appropriate control strategy and adjusting control actions promptly. This helps to enhance efficiency in control of the in-situ bioremediation process at petroleum-contaminated groundwater systems.

A data mining integrated architecture for shop floor control

The dynamics of the market are forcing shop floor control systems to enhance their intelligence and reactivity to change. The control system must also have mechanisms to process the vast amount of data, which is generated with the application of cheap data logging systems and should also incorporate a mechanism for continuous learning. Data mining provides a mechanism for analysing data and generating new knowledge. This paper presents an intelligent knowledge-based decision support system for shop floor control, which incorporates data mining techniques and intelligent agent technology to provide useful information, knowledge, and a mechanism for continuous learning. The functioning of the system is described and the architecture of different agents that constitute the system has been discussed in terms of the different modules required. A process to integrate the individual data mining agents associated with the different processes has also been described. Finally a prototype of the system is presented to demonstrate the application of this agent-based system.

A monitoring and diagnostic expert system for carbon dioxide capture

The research objective is to design and construct a knowledge-based decision support system for monitoring, control and diagnosis of the carbon dioxide capture process, which is a complicated task involving manipulation of sixteen components and their operating parameters. Since manipulation of critical parameter values directly impacts performance of the plant and carbon dioxide capture efficiency, it is important to effectively monitor, control and diagnose the process. This paper describes development of a knowledge-based decision support system for the carbon dioxide capture process. The knowledge acquisition process was conducted based on the Inferential Modeling Technique, and the knowledge-based system was implemented with G2. Since the reboiler heat duty is the most significant parameter influencing the carbon dioxide production rate, in the current version, the Carbon Dioxide Capture Monitoring and Diagnostic (CDCMD) system controls the heat duty to maintain the desired carbon dioxide production rate, thereby improving performance of the plant and enhancing efficiency of the carbon dioxide capture process. The CDCMD system provides decision support to the operator in monitoring the process; it can also function as a tutorial system for novice operators.

Development of an Ontology for an Industrial Domain

This article presents a method for ontology construction and its application in developing an ontology in the domain of natural gas pipeline operations. Both the method, as well as the application ontology developed, contribute to the infrastructure of Semantic Web that provides semantic foundation for supporting information processing by autonomous software agents. This article presents the processes of knowledge acquisition and ontology construction for developing a knowledge-based decision support system for monitoring and control of natural gas pipeline operations. Knowledge on the problem domain was acquired and analyzed using the Inferential Modeling Technique; then the analyzed knowledge was organized into an application ontology and represented in the Knowledge Modeling System. Since an ontology is an explicit specification of a conceptualization that provides a comprehensive foundation specification of knowledge in a domain, it provides semantic clarifications for autonomous software agents that process information on the Internet.

A KNOWLEDGE-BASED SYSTEM FOR STENCIL PRINTING PROCESS PLANNING AND CONTROL

ABSTRACT Surface mount technology (SMT) is one of the most important developments in electronic industry. A surface mount assembly (SMA) has three consecutive manufacturing steps: solder paste stencil printing, component placement, and solder reflow. Stencil printing process (SPP) involves highly operation complexity and has multiple quality characteristics, and averagely accounts for 60% of soldering defects in SMA. This work presents a knowledge-based system for SPP planning and control to upgrade soldering quality level and system performance. A hybrid data set contains a 38-3 experimental design and statistical process control (SPC) records was collected firstly, and followed by data processing for removing conflicted, inconsistent, and redundant samples through a fuzzycluster algorithm. The output columns of the clustered data were then transformed using fuzzy quality loss function (FQLF) with respect to the dissatisfaction of printing performance. The neuro-fuzzy technique was adapted to model and learn SPP knowledge from the transformed data set into a SPP knowledge base. Finally, a GUI man-machine interface was developed to help engineers in predicting responses and evaluating the overall SPP performance. The empirical evaluations of soldering quality and productivity demonstrate the effectiveness and efficiency of this proposed system.

A METHODOLOGICAL APPROACH TO KNOWLEDGE-BASED CONTROL AND ITS APPLICATION TO A MUNICIPAL SOLID WASTE INCINERATION PLANT

ABSTRACT In this work we have developed a knowledge-based combustion control system for a municipal solid waste incinerator plant. A dynamic model for a grate furnace has been proposed to analyse the relationships between the variables and obtain the rules for the knowledge bases. The input variables used were: furnace temperature, furnace pressure drop, power generated and oxygen content in the combustion gases. On the other hand, the output variables were: the actions that the operator should perform for waste feeding, the flow and the combustion air temperature with the aim of controlling the furnace temperature. The support tools used to control the process were abstractors, ALCMEN (Automaticians Language for Causal Modelling for Expert Knowledge) and SimCEES (C++ Embedded Expert System). To validate them, the real values of the furnace temperature have been compared with those obtained using a simplified combustion mathematical model and those obtained using the expert system.

Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.

Knowledge-based system for safety control of damaged ship

The maritime casualties cause serious consequences such as loss of life, property and maritime pollution. Therefore, the large-scaled casualties will be derived from loss of structural strength and stability due to the progressive flooding and enlargement of damage by the effect of wave and wind. The improvement of damage survivability is very important in maritime safety. In this paper, the knowledge-based system which could improve the safety level of stability and longitudinal strength through posture control of a damaged ship is studied. The system has been incorporated to interface with user to develop an ‘operationally practical’ action list based on ballasting to improve the ship's structural safety and stability status.

Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems.

A Knowledge-Based System for Supervision and Control of Regional Voltage Profile and Security

This work presents a knowledge-based system for supervision and control of regional voltage profile and security using fuzzy logic. The control strategies are defined by system operators based on their experience and on off-line studies, which are translated into rules of a hierarchical fuzzy inference system (FIS). Two hierarchical levels, namely, task-oriented control level (high level) and set-point control level (low level) compose the control structure. The high-level control is comprised of a continuous FIS that updates high-side voltage set points at power plants, and a discrete FIS that switches capacitor/reactor banks at the transmission network. The low level control is comprised of automatic voltage regulators and joint var controllers at the power plants. It is presented a simulation study in the Rio de Janeiro (Rio) Area, an energy importing region part of the South/Southeastern Brazilian system.