Scada is a genus of clearwing butterflies, named by William Forsell Kirby in That means t hat SCADA is technology -dependent and it is not a standalone science but it is the r e-sult of integrating variety of applied sciences such as communication, computers, software engineering, net-working, security, etc. Self paced learning and Instructor led live online classroom training options available. DCS provides real-time usage monitoring through AmeriFind's system activity reporting and also offers customizable combined reports for large accounts. How does it work?
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Industrial Energy Management by Using an On Line ToolVIDEO ON THE TOPIC: Industrial Communication Networks - Basis of Digitalization
The energy system is based around five steam headers and two cogeneration plants producing steam and electricity. Since the electrical system poses one of the main economic trade-offs with a steam system, electrical deregulation provides many new challenges to operate the overall combined system at minimum cost.
In addition, Kyoto protocol introduces a new motivation to reduce CO2 emissions. This paper describes the tasks performed, together with Soteica, using modern on line information system tools to assist with the energy system management. A full model of the energy system has been done. All the constraints have been included and the model is continually being validated with live data. Performance monitoring is done and it includes the tracking of equipment efficiencies by utilizing validated data for its continuous calculation.
By auditing the energy system, imbalances can be identified and reduced. Therefore, the data can be relied on for evaluating the value of energy production and usage, and waste can be eliminated.
Planning for a better operation of the energy system by performing case studies is usually done by using the validated model. Finally, it is important to mention that the optimization of operating conditions on a day to day basis is performed. As a result of the project, new sensors have been located and substantial savings in global energy costs have been reported. Refineries and large petrochemical complexes usually operate complex energy systems.
For example, they use different kind of fuels, operate cogeneration units, have several steam pressure levels, feed different types of consumers and there are emission limits to be observed. In addition, the Kyoto protocol introduces a new motivation for industry to reduce CO2 emissions in many countries, with particular consideration given to the CO2 emission cost and how it should be taken into account when managing energy systems.
In general, these complex energy systems have several degrees of freedom. Manipulating these degrees of freedom with a cost based optimization program usually can result in significant savings in operating costs. This is particularly important within current deregulated electrical markets.
Since the electrical system is one of the main economic trade-offs with the steam system, electrical deregulation provides many new challenges to operate the overall combined system at minimum cost. Other important aspects are that utilities systems are continuously evolving changes are frequent and that also, sometimes, there is a lack of sensors that needs to be addressed properly.
Furthermore, utilities systems have several constraints coming usually from the operations side. For example, maximum flows and steam production cushions. Finally it is important to mention that traditionally, given the complexity of the system, the optimization of the utilities is managed at the level of refinery areas. But the optimization of individual areas optimization does not necessarily give the true global refinery optimum.
In order to successfully address all the items mentioned above, a tool called Visual MESA has been used as the model and optimization engine. It is an online program that receives live plant data from the steam, fuel, condensate, BFW, and electrical system metering. Visual MESA helps to find the most economical way to run the utilities system, while remaining within the real operating constraints. Visual MESA provides a number of monitoring features that help to access data, control data quality, and alert of changes to the system.
Some examples are:. The energy system is based around five steam headers and two cogenerations plants producing steam and electricity. The steam network includes a "very high pressure" steam header, "high pressure" steam headers, two levels of "medium pressure" steam headers and "low pressure" steam headers. The "high pressure" steam is generated mainly in four boilers, the heat recovery steam generator at the other cogeneration unit, the Calcination unit, FCC and Hydrogen boilers.
The major high pressure steam users are the FCC and Coke units, Fuel oil heaters, a turbo generator and the cooling water system area. One of the levels of the "medium pressure" steam headers is supplied by letdown from higher levels through turbines or letdown valves and by the turbogenerator. Users of this steam level are almost all the users at Refinery area Bens. The other level of "medium pressure" steam is supplied by letdown from upper levels by let down valves or turbines and by the FCC, Coke, Vacuum and the medium pressure heat recovery steam generator of one of the cogeneration units.
The "low pressure" steam headers are supplied by letdown from upper levels let down valves and turbines , some steam generators and also flash tanks.
The users of this pressure level steam are units in both Conversion and Refinery areas. Electricity generation is performed by the two cogeneration units, including turbo generators. The refinery is usually an exporter of electricity.
The refinery is also able to import electricity if necessary since it is connected to the electricity external grid. The electricity system is especially interesting because of the current market which works on an hourly basis.
With respect to the fuel gas network, there are several suppliers and consumers of fuel gas being the most important consumer the gas turbine of one of the cogeneration units.
When the fuel gas density or pressure decreases, propane is automatically added to the network. The main objective of the project was to have available a tool for the on line optimization, auditing and monitoring the energy system. Such a tool could be also used for engineering studies such as evaluations of operational changes, investment projects, and shutdown and startups, taking into account both the technical and the economical impact on the energy system.
In the following paragraphs, first the model building and optimization implementation are briefly described. After describing the software installation and system architecture, the reports generation and their use for energy management are explained.
A complete model of the overall Energy System was built. The model includes the whole fuel, steam, boiler feed water, condensate and electrical system. Steam is generated in several different Units, with conventional boilers, heat recovery steam generators and a two cogeneration unit with possible steam injection. There are also two steam turbines that generate electric power. The five steam pressure levels were modeled as well as all the units with a high level of detail, including all the consumers and suppliers to the respective steam, BFW and condensate headers.
Electricity and fuels supply contracts details have been included in the model obtaining the electricity market cost from the real time data base system. By navigating through the model, each individual Unit of the system can be monitored in detail. The fuel gas network was also modeled, as it is involved with the steam and power generation and all its constraints and degrees of freedoms are also taken into account by Visual MESA.
It is shown on Figure 3. Visual MESA has built-in mathematical models and optimization routines in order to calculate how to run the steam and electrical systems at the minimum overall cost and still meet the required plant steam demands and other plant constraints.
The optimization determines where to make incremental steam which boilers or steam generators and which turbines or letdown valves will most efficiently let the steam down between pressure levels. Visual MESA optimization can be organized into four levels:. A well tuned model would generally be run at level 3, with a run at level 4 once in a while to evaluate potential operational changes.
The SQP optimizer's job is to minimize this objective function subject to operating constraints in the system. Total fuel cost is determined from the fuel use of each boiler and combustion turbine multiplied by their respective fuel prices.
Total electric cost is determined from the net electric use of each motor, load, and generator multiplied by their respective electric prices. The electric generation power selling is just negative electric use. The model takes into account the electricity price corresponding to the actual hour of the day as well as the penalty associated to selling more or less of the market arranged exportation set point.
Miscellaneous costs are normally used to charge for demineralized water coming into the system, but can be used for any other cost related to the energy system for example CO2 emission cost.
A model to calculate and optimize the CO2 emission cost has been developed and could run together the electric, steam and fuel optimization and help choose the best fuel to use in boilers and gas turbines taking into account the emission costs. The fuel gas network was also modeled to consider the complex constraints and different fuel availability. MS Excel provides a familiar environment for users to generate reports and views of the Simulation and Optimization of the system.
In the worksheet shown, steam generation and fuel consumption are reported for the current values and the optimized ones. Data comes to Excel directly from the model. NET technology, it can seamlessly link the Excel spreadsheet with the actual data and optimum operation calculated by the model. Visual MESA also generates automatic HTML reports in order to allow to everyone on the intranet network to see how the utilities system is being operated and what the potential savings are.
Therefore, the data can be relied on for evaluating the value of energy production and usage, and wastes can be eliminated. Visual MESA helps to find where wasteful steam use is occurring in the steam system. A Balloon performs the algebraic sum of all the flows for streams entering and leaving the balance.
Since we have a value for the flow of every stream, the total should be 0. If the net balance is not 0. Balloons dynamically show error by changing size and color depending on the amount of steam imbalance.
A Balloon is connected to each portion of a steam header where the possibility exists to close a mass balance. Figure 5 shows an example. A closed mass balance is formed by: A group of flowmeters, equipment with associated meters, or a combination of equipment and flowmeters.
CO2 emissions cost has been taken into account according to the Kyoto protocol. A special modeling block with the emissions factor for each fuel and the CO2 cost has been added.
Visual MESA adds the CO2 emission cost to the total cost equation, so when Visual MESA minimizes the total cost, it is taken into account with all other costs fuels, electricity, dematerialized water , and the optimum fuel feed to boilers and gas turbine is suggested. In principle, as the energy cost reduction is given mainly by a reduction of fuels consumption, this always implies a CO2 emissions reduction, except in the case where the optimization recommendations are related to the consumption of a cheaper fuel but that generates more CO2 instead of using a more expensive fuel that generates less CO2.
This could be the case when replacing Natural Gas with a heavy liquid fuel. This challenging tradeoff is affected directly by the CO2 allowance price. Visual MESA helps to evaluate potential capital or operating changes to the Refinery and assess the economics and operability of the changes. During a What If the current site status or also the corresponding optimum calculated by Visual MESA can be compared with the optimum case study and easy analyze how this change impacts on the site and how to operate the energy system after that change.
The evaluation of the economical impact of adding a new Natural Gas line feed to the Gas Turbine has been done. This investment project would allow the replacement of its Gas Oil feed. By running this What If study significant savings have been identified and of course a CO2 emissions reduction as well. Another What If example is how to operate the Refinery when one plant is shut down. For instance, a unit that is a high steam supplier for the refinery see the plant area called "AL" in Figure 1.
If this plant is shut down, the refinery has to adjust its steam production in order to adapt to the new situation. Working with the model in Stand alone mode, with the current operation values, run the optimization and set the results as the base case. The solution can be observed through reports, and also by different views in the model.
As the digital transformation of process industries has unfolded the last few years, wireless device-level networks are an important implementation element, extending the reach of higher-level networks to the process itself. The Internet in and of itself connotes wireless Ethernet such as Wi-Fi, but process industry complexes have enormous populations of field instruments and actuators using analog and digital fieldbus communication. Wi-Fi has not been adopted for these devices and there are no signs of a change in that direction. What has happened is the adoption of wireless communication for device-level networks. The leading protocol for this purpose is WirelessHART, which has grown steadily in hardware availability and deployments since its first release in , and its adoption as global standard IEC in Emerson Automation Solutions uses WirelessHART, but since it is a global standard, all devices certified by the FieldComm group interoperate regardless of the supplier.
Glossary of Advanced Manufacturing Terms
Although measures have been implemented to strengthen cyber defenses, they were not enough to prevent malware from infecting two Iranian petrochemical complexes last summer. This incident arises within a tense international setting surrounding the nuclear question. The petrochemical industry transforms and uses the chemical components of petroleum and its derivatives. The compounds derived from the chemical synthesis of petroleum, natural gas and biomass are used to manufacture many everyday objects: Plastic materials, textile fibers, detergents, solvents, medicines, food coloring and even fertilizer are made in petrochemical plants. The sector represents a key link in modern industry: It helps ensure the viability of many industrial sectors in a wide range of fields such as construction, automotive, healthcare, computers and even household appliances.
Information Technologies and Robotics Faculty
This book gives a wide-ranging description of the many facets of complex dynamic networks and systems within an infrastructure provided by integrated control and supervision: envisioning, design, experimental exploration, and implementation. The theoretical contributions and the case studies presented can reach control goals beyond those of stabilization and output regulation or even of adaptive control. Reporting on work of the Control of Complex Systems COSY research program, Complex Systems follows from and expands upon an earlier collection: Control of Complex Systems by introducing novel theoretical techniques for hard-to-control networks and systems. The major common feature of all the superficially diverse contributions encompassed by this book is that of spotting and exploiting possible areas of mutual reinforcement between control, computing and communications. These help readers to achieve not only robust stable plant system operation but also properties such as collective adaptivity, integrity and survivability at the same time retaining desired performance quality.
Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization. Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics. Reaction—diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication Spatial evolutionary biology. Rational choice theory Bounded rationality Irrational behaviour. A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate , organisms , the human brain , infrastructure such as power grid, transportation or communication systems, social and economic organizations like cities , an ecosystem , a living cell , and ultimately the entire universe. Complex systems are systems whose behavior is intrinsically difficult to model due to the dependencies, competitions, relationships, or other types of interactions between their parts or between a given system and its environment. Systems that are " complex " have distinct properties that arise from these relationships, such as nonlinearity , emergence , spontaneous order , adaptation , and feedback loops , among others. Because such systems appear in a wide variety of fields, the commonalities among them have become the topic of their independent area of research. In many cases, it is useful to represent such a system as a network where the nodes represent the components and links to their interactions.
Scada Vs Dcs
This glossary is intended as a practical and easy-to-use guide to common terms used in the advanced manufacturing industry. While we have made every effort to present current and accurate definitions, the glossary should be considered as a resource and not as an authoritative reference. Because the industry is ever evolving and complex, it is impractical to include every applicable term.
The book focuses on the advancements of processes, technologies, and approaches employed in distributed computer control systems DCCS. Discussions focus on system architecture, hot strip process, software structuring, and man-machine interface. The text then examines distributed microcomputer control systems for electrical power plants; distributed versus centralized computer control systems of industrial continuous process; and practical considerations for design and implementation of distributed digital control. The text takes a look at the architectural considerations of DCCS and its use in scientific experiments. Topics include system interaction software for the ECN, architectural schemes of DCCS, comparison of DCCS and multiprocessors, generalization of the concept of parallelism, and combined architectural realization of parallelism. The partitioning and synchronization concepts for computing dynamical systems algorithms on distributed computer control networks and scheduling of DCCS for industrial robots are also discussed. The selection is a vital reference for readers interested in distributed computer control systems. Distributed computer control systems proceedings of the third
Automation, robotics, and the factory of the future
The ESCAPE series serves as a forum for engineers, scientists, researchers, managers and students to present and discuss progress being made in the area of computer aided process engineering CAPE. European industries large and small are bringing innovations into our lives, whether in the form of new technologies to address environmental problems, new products to make our homes more comfortable and energy efficient or new therapies to improve the health and well being of European citizens. Engineers, scientists, researchers, managers in the chemical, pharmaceutical, and biochemical industry involved in computer assisted process engineering. Modeling the liquid back mixing characteristics for a kinetically controlled reactive distillation process. Three-moments conserving sectional techniques for the solution of coagulation and breakage population balances. Modelling of micro- and nano-patterned electrodes for the study and control of spillover processes in catalysis.
Scope of Technical Committees
Accidents and natural disasters involving nuclear power plants such as Chernobyl, Three Mile Island, and the recent meltdown at Fukushima are rare, but their effects are devastating enough to warrant increased vigilance in addressing safety concerns. Nuclear Power Plant Instrumentation and Control Systems for Safety and Security evaluates the risks inherent to nuclear power and methods of preventing accidents through computer control systems and other such emerging technologies. Students and scholars as well as operators and designers will find useful insight into the latest security technologies with the potential to make the future of nuclear energy clean, safe, and reliable. Head of the Department of aerospace control systems, Kharkiv Military University Invited lecturer of a lot of international conferences and universities.
Code of Department. Code of subdepartment. IU Automatic Control Systems for Flight Vehicles.
The energy system is based around five steam headers and two cogeneration plants producing steam and electricity. Since the electrical system poses one of the main economic trade-offs with a steam system, electrical deregulation provides many new challenges to operate the overall combined system at minimum cost. In addition, Kyoto protocol introduces a new motivation to reduce CO2 emissions. This paper describes the tasks performed, together with Soteica, using modern on line information system tools to assist with the energy system management.
Critical infrastructures, such as electricity generation plants, transportation systems, oil refineries, chemical factories and manufacturing facilities are large, distributed complexes. Plant operators must continuously monitor and control many different sections of the plant to ensure its proper operation.
ARAMIS is a pluridisciplinary group bringing together methodological researchers computer science, applied mathematics and medical experts neurology, medical imaging. The general objective of the team is to build numerical models of brain diseases from multimodal patient data medical imaging, clinical data, genomic data.