THE YIELD IMPROVEMENT TECHNOLOGY A REVOLUTIONARY TOOL FOR TOTAL QUALITY MANAGEMENT (TQM)

Transcription

1 THE YIELD IMPROVEMENT TECHNOLOGY A REVOLUTIONARY TOOL FOR TOTAL QUALITY MANAGEMENT (TQM) K. Elis Norden Institute of Yield Technology Ltd Grossfeldstrasse 76, CH-7320 Sargans, Switzerland Abstract Yield analysis in production processes is nothing new, but intelligent utilization of modern technology has opened possibilities for yield improvement within the frame of the Total Quality Management (TQM) concept through processing of weighing and chemical analysis data in real time. The paper presents a new yield improvement technology concept to continually optimize the process yield and improve the precision of aim in the chemical analyses, and ends with economical aspects at the implication of the new technology. PREAMBLE Steel making has become an extremely competitive business. Many steel plants have already been shut down, and the end of the tunnel is not yet in sight, so according to pessimistic or maybe realistic forecasts one must count with further considerable closedown of the steel industry within the next decade. It is only the most efficient steel plants that will survive, and to stay competitive it is essential to maximize sales revenues by maximizing productivity through, among others, yield improvement, and minimizing the costs for material and labor. A prerequisite is of course that the products have as great demand quotient as possible, and are manufactured with a high and consistent quality. Many theories have been tried to obtain better results, and the Total Quality Management (TQM) concept is a modern approach to achieve higher revenues and better product quality through improvement of all components in the production chain: EQUIPMENT RAW MATERIALS PRODUCTION PROCESS PRODUCT PERSONNEL Fig. 1: Production chain Improvement in total quality is a very intangible subject, extremely difficult to define and quantify. In most cases it can only be pinpointed by improvement in yield, which in the production process is a measure of performance, defined through: Output weight Y M = 100 % (1) Input weight This equation presents the yield only on a material basis, however, more important is the economical yield, defined through: Proceeds of sales Y E = 100 % (2) Production costs

2 The economic yield (Y E ) contains not only the material quantities, also the production costs, with salaries, costs of energy, depreciation, material losses etc are included. The yield figures can as well be used to determine improved productivity, i.e. increased output with the same input of raw materials, as improvement in economical result, or to rate human management performance. Yield improvement applied to - Raw materials - can for example be achieved by choosing a cheaper raw material, such as classified scrap with a certain alloy analysis, to come as close as possible to the required analysis specification of the finished steel, and thereby minimizing the need of more costly alloying materials. In the - Production process - yield improvement can be obtained by minimizing the squandering of materials in each step of the process, and/or by producing closer to the specification with regard to analyses, or with regard to bloom, slab or billet weights in the continuous casting, or to the weight/meter limits in hot rolling mills etc. With regard to - Equipment - yield improvement can be accomplished for instance by utilizing weighing equipment with the weighing accuracy optimally adapted to the requirements of the process, which might as well reduce equipment and material costs, as make it possible to produce closer to specification requirements, and to be right the first time. Motivation of - Personnel - which is an important factor within the TQM concept, but presenting a main problem with regard to the appraisal of the performance, can also be stimulated through continuous measurement of the yield in the process they control. Yield figures - reliability With the direct measurable yield figures according to equation (1) the changes in yield from heat to heat in the steel making process can be established. A prerequisite is, however, that the material weights are accurate enough to reliably establish the small changes in weight. In the past, yield figures have usually been obtained through statistics representing history, and they have furthermore been impaired by too great uncertainty to veraciously indicate yield improvements. Even today the weighing accuracy in many steel plants is not sufficient to establish reliable yield, and often theoretical weight, for instance of billets or slabs cut on length, and so on, are being used. Despite these inaccuracies, the yield is often presented to one or even two decimal places, although such yield figures represent more wishful thinking than reality, as shown in table (I) below. Y 1 Y 2 Y 3 Output weight: Input weight: Yield: 72 t ± 2 % 83 t ± 2 % 86.8 % ± 3.5 % 72 t ± 1 % 83 t ± 1 % 86.8 % ± 1.8 % 72 t ± 0.1 % 83 t ± 0.1 % 86.8 % ± 0.2 % Table I: Illustration of erroneous accuracy of process yield The output and input weights are in all three cases the same, but the weighing errors however, vary between ± 0.1 % to ± 2 %, and thereby the maximum spread or uncertainty in the yield figure varies between ± 0.2 % to ± 3.5 %. It is obvious that it is directly misleading to present (Y 1 ) and (Y 2 ) with a decimal, as even the unit figure is doubtful. In addition to this, the uncertainty is of the same magnitude as a possible yield improvement, so that these yield figures are unusable as a basis for yield improvement actions. Even (Y 3 ) is on the limit regarding the spread, but this figure can be tolerated for comparison purposes in a reiterating process. It is therefore important to be very cautious in connection with yield figures - the reliability is directly related to the accuracy of the weight figures involved.

3 THE YIELD IMPROVEMENT TECHNOLOGY Yield measurement has during the past decade gained new dimensions of accuracy through advances of computerized electronic weighing and the ability to process the weight data and present the results in real time. However, electronic weighing as such does not improve the yield in a manufacturing process, but systematically amalgamated in the production chain it provides a tool to detect where losses or squandering occurs. Furthermore, through accurate weighing of the material flow, yield figures can be calculated and presented to the operators in real time, so that appropriate actions for improvements through gradual optimization at the different steps, and thereby the whole process, can be made. A secondary effect of the yield analyses can be obtained through its combination with the chemical analyses of the steel to automatically calculate and present the Precision of Aim in alloying. By obtaining the required analysis through an optimized alloying procedure, savings in alloying materials and improvement in quality by narrowing the tolerance margins can be achieved. With the new technology for yield improvement, a global yield improvement for the whole process is successively accomplished through gradual reduction of losses and optimization of performance in each step of the production process, from heat to heat in real time. To achieve the operational performance required it is necessary to define and create an integrated and syntonized system of all operational components throughout each step of the process, from raw material input to output of the finished product, including material handling, weighing, data processing and presentation, as well as human supervision and control, based on operational performance data and requirements, i.e. application of the Total Quality Management. Technical audit - key to technical advances The logical first step in implementing the Yield improvement technology is to make a Technical Audit. This is used to: 1. Analyze the present process and outcome, with regard to methods and equipment presently being used, as well as influence from human intervention. 2. Define the best way with regard to losses and squandering to divide the process into groups for partial yield analyses, and based on theoretical calculation of requirements and present performance define the necessary improvements. 3. Lay the ground for an integrated operational system with hardware and software syntonized to step by step in the process successively improve the yield and thereby optimize the whole process. 4. Lay out and specify the individual equipment hardware and software, with technical data derived from the system requirements according to item 2. above, and define the human contribution in the process. In analyzing the present system and laying ground for the new integrated system, a flow chart as shown in Fig. 2. is made. This shows each event of the material transfer and treatment, as well as collection of process data and processing throughout the whole production process. The flow chart illustrates as an example, the initial part of the material flow in an EAF steel plant, with delivery of scrap and raw materials, charging of scrap and batching of carbon and lime into the EAF, followed by tapping of the liquid steel and alloying in the ladle during tapping.

4 Yield analysis flow chart DELIVERY OF ALLOYING MATERIAL DELIVERY OF FLUX ADDITIONS DELIVERY OF SCRAP TRUCK & RAILWAY SCALE W0 SAMPLING SAMPLING SAMPLING ANALYSES A0 STORAGE RAW MATERIALS SCRAP ALLOYING MATERIAL FLUX ADDITIONS SCRAP BATCHING CARBON ALLOY BATCHING SYSTEM B1 BATCHING LIME INJECTION SCALE B2 SCRAP BASKET SCRAP SCALE W1 ELECTRIC ARC FURNACE SAMPLING ANALYSES A1 TEMPERATURE PYROMETER T1 TAPPING LOSSES SLAG + LIQUID STEEL EMPTY LADLE LADLE TARE CRANE SCALE W2 BATCHING ALLOYS ALLOY BATCHING SYSTEM B3 LADLE WITH SLAG +LIQUID STEEL SAMPLING SLAG DEPTH ANALYSES SLAG WEIGHT A2 S1 LADLE WITH SLAG + LIQUID STEEL CRANE SCALE W3 TRANSFER TO SECONDARY METALLURGY Fig. 2: Flow chart for yield analysis in an EAF melting shop The ladle with liquid steel and slag is weighed in a crane scale during transfer for further treatment in the secondary metallurgy.

5 DATA PROCESSING Yield calculation at the EAF All weight data (W0)... (W3), (B1)... (B3) and analyses (A0)... (A2), as well as temperature (T1) and slag weight (S1) are transferred on-line from the scales etc, to an EAF Computer. Using the designations in the flow chart, the computer makes the following calculations of the metallic yield (Y EAF ) to determine the losses by charging the EAF and tapping in the ladle: W 3 - W 2 - S 1 - B 1 - B 2 Y EAF = 100 % (3) W 1 + B 3 Legend: (W 1 ) = Scrap charge (W 2 ) = Ladle tare weight (W 3 ) = Ladle gross weight (S 1 ) = Slag weight (B 1 ) = Carbon weight (B 2 ) = Lime weight (B 3 ) = Alloying material weight Similar calculations are made at each point where losses occur throughout the process, providing information, which can be used to reduce the losses and improve the yield. Most modern steel plants make data logging of various parameters, including weights, analyses etc, which are processed through computers and presented in heat reports and periodical reports. Often these reports contain a global yield figure, based on input weights of scrap and raw materials and output weights of billets or slabs. Usually these reports are analyzed at a later time (mostly the day after), whereby they represent history. This is useful for instructional purposes and for controlling the process on a long-term basis, but not for immediate corrective actions to improve the yield. To achieve successive yield improvements, the yield after each heat must be calculated in real time, so that changes from one heat to another can be used for appropriate actions to improve the yield in the next heat. Yield analyses in real time To be able to present the change in yield from one heat to the next in an understandable way, from which the operator can draw conclusions for appropriate actions, the computer will also calculate and present in real time: n _ Σ Y i=1 The arithmetic mean value: Y = % (4) n _ Deviation from mean value: δy n = Y - Y n (5) Trend: T = δy (n-1) - δy (n) (6) The presentation on the VDU can be made alphanumeric or graphic (or both), so that the operator will get the information in a form, which he can interpret to take the necessary action.

6 PRECISION OF AIM IN THE ALLOYING There are areas in the production process where the losses normally are very small, for instance in the secondary metallurgy at the Ladle furnace, or at a Vacuum treatment installation, whereby the yield is practically 100 %. In such a case the calculation of yield is impractical, but the calculation of the precision of aim will also under these circumstances provide useful information. Calculation of the precision of aim in the alloying can be made in the same manner, to provide correction factors for the batch weights in the following charge, resulting in saving of alloying material, and improving the quality by reducing the scattering and thus narrowing of the tolerance limits for the analysis. An example based on the Mn content will illustrate the procedure, which of course can be used for any alloying material. Scrap: The Mn content in the scrap charge is: n Mn S = S X A SX kg (7) x=1 (S X) = Partial weight of each scrap quality (A SX) = Mn analysis in the different scrap qualities. In many steel plants the different scrap qualities are not segregated with regard to analyses, i.e. the analysis values for the different scrap qualities are not known. In such a case the theoretical Mn content in the scrap can be calculated by taking the analysis value for hot metal in the EAF, and using the theoretical metallic content in the EAF, i.e. the weight of the scrap charge: Mn S = S A kg (8) (S) = Total charge of scrap (metallic content). (A) = Mn analysis in the hot metal. Alloying material: The Mn content in the total charge of alloying material is: n Mn C = C X A CX x=1 kg (9) (C X) = Partial weight of each alloying material. (A CX)= Mn analysis in the different alloying materials. The total charge: The theoretical Mn content in the charge of scrap and alloying material is: Mn T = Mn S + Mn C kg (10) The liquid steel: The effective Mn content in the liquid steel is: Mn E = W LS A LS kg (11) (W LS ) = Weight of the liquid steel. (A LS ) = Mn analysis in the liquid steel.

7 Calculation of the Precision of aim The influences from oxidation, as well as from weighing and batching errors (fines), which usually remain constant from one heat to another, can through the calculation of the precision of aim (P) be established: P = Mn E 100 % (12) Mn T The same calculation can of course be made for any alloying material, and at each step of the process, providing a correction factor to compensate for the above-mentioned influences. Correction factor For correction in a following heat, the computer can automatically calculate and implement a correction factor (F) for the set point of the alloying material according to the following equation: 1 F = _ 100 (13) P REQUIREMENT ON AND ANALYSIS ACCURACY IN THE STEEL MAKING For industrial scales it is realistic to count with weighing errors of at least ± (3d) in service, and ± (2d) at the initial verification, (d) being the digital increments of the scale. However, the investment cost for a weighing system, and the costs for maintenance and service are rising considerably with increased weighing accuracy, which therefore should be determined to a meaningful level with regard to the production process. Applied to a metallurgical process, this means that if for instance the analysis of the liquid steel indicates that a certain ferroalloy should be added, there is no need to have a better weighing accuracy than what the analysis equipment reliably can discriminate, i.e. the analysis accuracy. On the other hand the weighing accuracy should be high enough, so that not too much or too little material is added. Optimum relationship: Weighing accuracy - Analysis accuracy An example will show how the required weighing, and analysis accuracy can be theoretically calculated to reach an optimum: Assume that a certain ferroalloy, or scrap, with the weight (Q) kg, and the analysis value (A) %, with the analysis error (α) %, is weighed on a scale with the weighing error (δ) %. Alloy content: The effective alloy content in the charge can be calculated: C = Q (100 A ± A δ ± 100 α) 10-4 kg (14) Weight error: Thereby, the weight error in the effective alloy content is: σ C =± Q (A δ ± 100 α) 10-4 kg (15) Total weight error in the charge: When several (n) alloying materials with different weights and analysis values are being charged into a ladle, the total weight error in the alloy charge is: n σ Cn = σ C kg (16) i=1

8 Weight error in the liquid steel In a ladle with the liquid steel weight (Q m) and the analysis (A m) for a certain alloy, and with the weighing error (δ m) and the analysis error (α m), the weight error in the alloy content is: σ m = Q m (A m δ m ± 100 α m ) 10-4 kg (17) An optimum in the requirement on weighing accuracy is reached when the total weight errors due to weighing and analysis errors in the charge and in the liquid steel are equal: σ C = σ m (18) With known analysis errors, the allowed weighing error for each scale can be calculated from equation (18). Note: The weighing error (δ) is the actual error at a certain weight (Q), and not the nominal error of the scale. Maximum permissible weight error at the batching of alloying materials In a ladle with the liquid steel weight (Q LS ), the analysis for Mn shall be increased with the value (A LS ). To achieve this a weight (C) of Ferro alloy with the analysis (A C ) will be required: Q LS A LS C = kg (19) A C Assuming that the liquid steel weight has a weighing error (δ m ) and the analysis value an analysis error (α m ), then the permissible weight error (σ C ) in the charge of alloying material can be calculated: Q m (A m δ m ± 100 α m ) 10-2 σ C = kg (20) A C This will be demonstrated on a practical example: To increase the Mn analysis with (A m ) = 0.3 % in a ladle with the liquid steel weight (Q m ) = 100 t, FeMn alloy with the Mn analysis (A C ) = 50 % will be charged, whereby the required charge weight of FeMn will be: C = = 600 kg 50 With the weighing error (δ m ) = 0.15 %, and analysis error (α m ) = 0.01 %, the permissible weight error in the charge of FeMn is: ( ) 10 σ -2 C = = 20.9 kg 50 which with a typical batching scale with the weighing range (W R ) = 1500 kg, (d) = 0.5 kg, and a maximum weighing error (δ) = ± 1.5 kg, should not cause any problem. This example also shows that if the analysis error would be (α m ) = %, the permissible weight error would be (σ C ) = 2.9 kg, which illustrates that the limit on the achievable accuracy is set by the analysis equipment and not by the weighing system.

9 IMPLEMENTATION OF THE YIELD IMPROVEMENT TECHNOLOGY In implementing the yield improvement technology the first step is to make a technical audit defined to process control, with the aim to increase profitability and product quality, and pinpoint eventual weaknesses in the existing process with regard to equipment hardware and software, as well as in human contribution. The audit contains theoretical analyses of operational requirements and present performance of the equipment hard and software, and concludes with recommendations with concrete plans for improvement, including layout of an integrated material handling, weighing and data processing system, as well as specifications with technical data of the hardware, and software menus for the operational proceedings. The fully integrated system can usually not be realized at once, but it is important to have a long-term plan over a period of years to gradually and systematically procure and install the hardware and software of the newly defined comprehensive system. The ultimate goal is to achieve higher profitability and better quality. In addition to this an impartial technical audit provides an unequalled tool to the management in comparing, i.e. "Benchmarking" different plants in a group, and for establishing the performance status, and the need of eventual further investments in take-over operations. Economical aspects - return on investment Measurement of yield throughout the steel making process, and the yield improvement achieved through partial improvements in the different steps of the process where losses or squandering occurs, as well as increased precision of aim in the steel chemistry, provides: Increased operational profit - with unchanged operational costs. Better control of charging and tapping procedures, with less squandering. Saving in operational time through higher precision of aim in the alloying procedures. Saving of raw materials through updating of charge weights from heat to heat. Improved optimizing procedures in continuous casting. Improvement in Human resource management by introducing individual performance measurement. Experience from assignments in more than 20 European and South American plants, including: Sintering plants, Blast furnaces, BOS plans, EAF plants, hot and cold Rolling mills, and Foundries, indicate attainable average yield improvement potentials of 3-5 % of annual sales. Example: In a steel plant with a production volume of 2 million tons/year and assuming a price of the finished product of c:a 200:-/ton, and a recovered scrap price of c:a 80:-/ton, i.e. with a turnover of 400 millions/year, a yield improvement of 3-5 % represents an increased profitability of 7-12 millions/year, with a need for investment in equipment hard and software of the order of 5-10 millions over a period of 2-3 years. CONCLUSION Several now-a-days well-known companies are marketing software programs for administration of the steel making process, from procurement of raw materials to planning of the production, to optimize on customer demand, and thereby reduce stock and operational time, through for instance utilizing the "just in time" principle.

10 The Yield Improvement Technology concept is on top of these administration programs providing an instrument to optimize the process yield at each instant where losses or squandering occurs, and integrated the global process yield, by calculating and presenting the yield at each heat in real time, so that the operative management from heat to heat can take appropriate actions to successively optimize the yield. * * * * *