Manufacturing Process Controls for the Industries of the Future (1998) / Chapter Skim
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2 Synthesis of Industrial Needs
2 Synthesis of Industrial Needs
Pages 14-27
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to identify common process attributes and needs for process controls and sensor technologies among the industries. This section includes descriptions of key manufacturing processes and the needs specific to six of the IOF industries.
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The glass industry's needs for improved sensors and controls are based on four key factors: · improving energy efficiency (energy accounts for 10 to 15 percent of manufacturing costs) improving product quality and productivity · improving environmental compliance · maximizing recycling and reducing waste (approximately 35 percent of glass is recycled from postconsumer waste)
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codes: industrial inorganic chemicals plastics, materials, and synthetics pharmaceuticals · soaps, cleaners, and toilet goods paints and allied products industrial organic chemicals agricultural chemicals miscellaneous chemical products The vision document for the chemicals industry, Technology Vision 2020: The U.S. Chemical Industry, identifies three principal areas for advancing chemicals manufacturing process science and technology, chemical measurement, and computational technology all of which are critically dependent on improved process modeling, control, and sensing.
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Casting processes can be used to produce complex shapes ranging in size from a fraction of an ounce to hundreds of tons (ASM, 1988~. The selection of casting processes depends on a range of factors, including required mold strength, component size, variations in the thickness of cast sections, required surface finish and dimensional accuracy, production rates, environmental factors (e.g., reclamation of sand and type of sand binder)
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Specific process sensing needs for the metal casting industry include (Green, 1996; Walkington, 1996) · robust sensors for measuring the temperature of molten metals · sensors for determining alloy composition, including dissolved-gas composition (i.e., chemical sensing in molten metals)
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· evaluating printability, a complex characteristic that depends on the characteristics of paper sheet (e.g., smoothness, absorbency, moisture content, formation, opacity, brightness, receptivity to ink, compressibility, surface energy, and mottle) In general, the challenge for process controls in the forest products industry is to design and implement control systems that provide for the intelligent optimization of processes and total mill control.
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The major processing steps used by the aluminum industry are listed below (Green, 1996~: mlmng alumina resoling aluminum reduction metal casting thermal treatments fabrication processes production of fabricated products secondary processes (recycling) The first processing step, after mining, is alumina refining, during which bauxite is refined to A12O3 using the Bayer process.
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· in-line measurement of alloy composition · detection of inclusions · continuous monitoring of hydrogen content below: The needs for sensors and controls in thermal treatment processes are listed · in-line chemistry measurement · in-situ combustion analysis for furnace control · accurate noncontact temperature sensors The key needs for sensors and controls for the remaining fabrication processes and for fabricated products include noncontact measurement of temperature (from 65 to 540°C) in-line texture analysis control of sheet shape and work roll temperature profiles measurement of physical dimensions high-speed, noncontact sensing of surface quality control of metal flow in extrusion and forging processes (requires mea surements of temperature gradients, surface contours, and residual stress, as well as accurate process models)
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On the basis of these objectives, the AISI subcommittee on sensors, which includes representatives from Armco, Bethlehem, Dofasco, Inland, Timken, Alcoa, and LTV, has identified needs for sensors in the steel industry. Process sensing in the steel industry must accommodate several key factors, including very harsh processing environments, the need for extremely accurate measurements, and, in some cases, the speed (acquisition, measurement, and reporting response time)
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Significant barriers to the development and implementation of new sensors and manufacturing process control technologies are listed below: · the cost and time required to develop, validate, and implement advances from prototypes to reliable process control systems · the risk to existing production environments, schedules, and rates from the introduction of new process technologies the lack of materials and fabrication processes for advanced sensors capable of operating in the harsh processing environments described above for each of the IOF industries · incomplete understanding of process physics . Industries may require validated simulative process models, or "plant models," before they invest in new advanced sensors or process control technologies.
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monitored by improved process controls and sensors. The cumulative investment for data recording, archiving, and visualization, in addition to the cost of maintaining analysis software, could exceed the periodic reinvestment by industry in new control hardware and software.
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Measurements and analyses must be rapid enough to allow feedback control of high-speed processes with minimal waste. Process Control Requirements As a basis for organizing requirements for sensors and corresponding process control technologies, the panel classified process controls into three levels of complexity (shown in Figure 2-1; Jones, 1997~: ~ it 1~11 ~1 1~11er~,16H,1~ 1 MY | Y r | ~ r |~ r l r |~Zu > umax ICY IDLY |~ u |
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Implementing technologies, such as control algorithms, specific process models, and actuator systems, will have to be industry specific. The process control requirements that are common to the IOF industries are listed below: · effective use of process measurements · process control methodologies that enable in-situ-level process control fuzzy logic controllers that can translate expert system rules to establish control parameters model-based controllers that use process models to establish control parameters · hybrid models that allow multiple, disparate process models to be used in a cohesive and integrated way · plantwide or enterprise-level optimization, including multi-unit controls and robust, self-diagnostic supervisory controllers · tools for open-architecture applications, including operator/controller interfaces that allow effective control of dynamic processes by non-experts · adaptive control systems that account for the variabilities of aging equipment, environmental conditions, or product mix REFERENCES Aluminum Association.
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From page 27... ...
1996. Sensor and Control Needs for the Metal Casting Industry.
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