Journal of Computer Engineering & Information Technology

Transcription

1 Mogaji, J Comput Eng Inf Technol 2014, 3:1 Research Article A Decision Support System for Process Planning and Control of Polyurethane Foam Production PB Mogaji * Department of Mechanical Engineering, Federal University of Technology, Nigeria * Corresponding author: PB Mogaji, Department of Mechanical Engineering, Federal University of Technology, Akure, Ondo State, Nigeria, pbmogaji@yahoo.com Rec date: Nov 11, 2013 Acc date: Mar 24, 2014 Pub date: Mar 27, 2014 Abstract Major sections involved in the production of Polyurethane foam are: Chemical reactions, raw material formulations, heat transfer during curing stage, and the temperature conditioning of the chemicals, and these are the major problems in the optimal production of Polyurethane foam. A Polyurethane foam industry in, was used as the case study for this research work. Data for process planning, manufacturing, and cost of polyurethane foam were collected, complimented by extensive literature survey carried out both from the library and through direct interview from domain expert. The data collected were used to model polyurethane foam process planning, raw material combination formulation, and manufacturing cost computation. A comprehensive flow chart for process planning and cost estimation of polyurethane foam was designed. This was used to develop a decision support system (DSS) for process planning and control of Polyurethane Foam production. The DSS developed was validated using the raw material combination formulation from the case study Company located in Southern part of Nigeria, with 1000kg of polyol with density 17kg/m3 and the same data was loaded into the decision support system developed. When the result were tested using paired t-test comparison, the result showed 2- tailed significance of 0.057; this connotes that there is no significant difference between the raw material formulation result generated by the company machine and the result generated by the DSS developed and confirmed the software to be reliable for raw material formulation. On the cost estimation, t-test shows a 2-tailed significance of 0.153, which showed that there is no significance difference between the manual cost estimation result displayed by the company and the result displayed by DSS, but the system developed is faster. Keywords: Decision support system; Process planning and control; Polyurethane foam and production Introduction The complexity of Polyurethane foam Production which requires monitoring of several factors, such as: chemical reactions, raw material formulations, heat transfer, and temperature conditioning of chemicals, necessitate that the production Engineer in this regards should be experienced and versatile in the production techniques. Details of the various aspects of production planning and control are covered in such work as; [1-3]. The conventional way of affecting Journal of Computer Engineering & Information Technology adequate production planning and control are also fully discussed in such works as: [4-6]. It is obvious from the above works both on the techniques of production planning and control, that it will take a good engineer to fully optimize the system of production. Also, repetitiveness of decision making is difficult as the caliber of the production engineer changes. Many analytical techniques presently employed in production planning and control may not be adequate in this regards. This is because they do not incorporate the human experience in such analytical or pseudo- analytical techniques. The model available for optimizing decision for its production can be iconic, mathematical, empirical, or deterministic and stochastic [7]. Most available models are unable to accommodate multifarious decisions often made by production expert, before, during and after production. The DSS is relatively new modeling techniques for inputting expert specialist knowledge into production planning and control. The application of DSS is applied in this study. Materials and Methods A SCITECHNOL JOURNAL The operational conditions and management decision for the production of Polyurethane foam were determined from the analysis of the data obtained from the Polyurethane foam production expert. The raw material parameters analyzed included chemical proportions, reactant temperature, heat generation and absorption and physical properties namely, density, hardness, and softness. Operational conditions of the production Machines related to chemical reaction, raw material formulation, and heat transfer were determined by imposing linear programming (LP) based operational constraints. The management decision relating to manufacturing cost, quality of foam, and demanded size of foam were determined using Machine Hour Rate (MHR), production process control, and demand pattern, respectively. The production process control was determined for Toluene-di- Isocyanate (TDI), Auxiliary Blowing Agent (ABA) with varying index measured based on ABA availability. The foam size corresponding to quality were transformed to regression model analysis by using density and water as process parameters, the operational parameters of the output foam were optimized using sensitivity analysis. The operational parameters were modulized in conformity with expert information from the polyurethane foam industry. Flow chart, Algorithm, and software coding (using Visual Basic 6.0, 2010) were carried out in conformity with the expert modules generated. The modules utilized were, raw material, processing, chemical reaction, temperature conditioning, quality control, manufacturing cost, and finished product. In the Decision Support System for Process Planning and Control of Polyurethane Foam Production (DSSPPCPFP), queries and facts were raised based on human expert advice. These were used to arrive at optimal decision that minimized the overall cost of manufacturing. The DSSPPCPFP developed was validated by verification of the software output results from the initial data, latest data, and practical utilization of same in the Polyurethane foam industry used as a case study. The output results were analyzed statistically using paired t-test from which the conformity of the developed expert system were compared with human expert working on the polyurethane foam production. All articles published in Journal of Computer Engineering & Information Technology are the property of SciTechnol, and is protected by copyright laws. Copyright 2014, SciTechnol, All Rights Reserved.

2 DSSPPCPFP Architecture A decision support system for process planning and control of polyurethane form production has been developed to enhance production efficiency in polyurethane foam industries. The DSSPPCPFP system architecture is shown in Fig. 1. Seven modules work together to support the decision-making task. The material selection module and the process selection module are order independent, thus both material first and process first selection schemes are supported. These modules evaluate the compatibility between each alternative and product profile requirements and output a partially ordered set of compatible alternatives. Chemical reaction and temperature conditioning modules are linked together to maintain temperature of the selected raw materials at required temperature between 22oC and 25oC. The quality control module check the quality of form in comparison with the specific standard set up in the data base of the software. Manufacturing cost module generate immediately the production cost of different category of form based on the cost parameter data in the data base. Finally, the finish product module display all the category of foam. Figure 1: Developed DSSPPCPFP module. Model Development The objective of the LP model is to minimize the total relevant costs. The algebraic formulations are presented below. The objective function represents the production cost. The constraint implies that the number of units of raw materials to be used in any time period cannot exceed the availability of raw materials in that time period. Objective function: ni Minimize Z = PjXj Cost, N (1) Subject to: Polyol βjxj b1 (2) Where b1 is the total available polyol TDI jxj b2 (3) Silicon γjxj b3 (4) Stannous Octate α jxj b4 (5) Amine δjxj b5 (6) MethylChloride φjxj b6 (7) Additional Constraints Additional restriction may be required based on the company experience or what is currently in operation in the company. For instance, customers demand for a specific quantity for a particular form type and size necessitate individual size production limit constraints. These can be expressed as follows: Minimum production limit Xj Ij (8) Maximum production limit Xj Ij (9) Non-Negativity Constraints The non-negativity constraints which state that a negative quantity of each size and type of foam cannot be is stated as Xj 0for IJ = 1 (10) Related overhead cost per unit of size j.. (11) Machine Hour Rate (MHR) (i) Salary of operator including allowances per month - C1 (ii) Overhead cost is assumed at 10% operator s salary/month - C2 (iii) Repair and maintenance cost of machine is assumed at 20% of the purchased cost - C3 (iv) Cost of working space if building is assumed last for 50 yrs - C4 (v) Repair maintenance of floor space cost - C5 (vi) Installation cost is assumed at 5% of purchase cost - C6 (vii) Annual Insurance cost I assumed at 0.5% of purchased cost-c7 (MHR) = C1 + C2 + C3 +C4 + C5 +C6 + C7 Page 2 of 7

3 For all Machines n M MHR = C=1 l=1 Ckl (12) Where, l is counter for machines l=1 m, k = 1 n, Counter for resources associated with each machine. Production cost per unit of size j PC j Where, 1 PCj = (a j +b j + cj + f j + n M C=1 l=1 Ckl (13) Note these parameters: a j = Direct materials cost per unit of size j b1 j = Direct labour cost per unit size j b i = Denotes the total availability of the ith resources C j = Direct expenses (rate of electricity, water and fuel) per unit size of foam i = denotes the ith resources j = denotes the jth size of foam mi = denotes the number of resources used in producing the foam ni =denotes the number of size of foam Pj = denotes the cost per unit of size j of foam X J = denotes the quantity of the jth size of foam Z = objective function β j = denotes cost of polyol required for unit of size j of foam σ j = denotes cost of TDI required for unit of size j of foam γ j = denotes cost of silicon required for unit of size j of foam α j = denotes cost of stannous octate required for size j of foam?? = denotes cost of amine required for size j of foam φ j = denotes cost of melthylchloride required for size j of foam Table1 below shows the standard specific proportion of density to water as revealed by Polyurethane foam industry used as case study. S/N WATER (Part by Weight) DENSITY (kg/m3) Table 1: Standard Specific Proportion of Density to Water Let Water be represented by Y, and Density, be X Y = ax (14) Where, a is constant. For normal polyurethane foam production (Flexible), constraints on water requirements can be formulated as: 5.2 y 2.0 (15) and, constraint on density as 40 x 18 (16) Number of hydroxyl (h) that will go into reaction with Toluene-diisocynate (TDI) and water is h = bj (17) Where, h = hydroxyl number, b = Constant, j = the volume of carbon dioxide librated. Amount of TDI that will go with density x and water y is μ = 9.67 y h (18) Where, μ = Total amount of TDI in the formulation, h = 46 h 47, i = index measured Base on constant without Auxiliary Blowing Agent (ABA) is 109 i 106 and constant with ABA is 120 i 110. Constant on part of water and ABA requirement Y = 1 ABA (19) 7 Blow index (ib) = y (20) Regression model analysis, Density (x) equals Density (x) = y (21) Actual Foam Weight Determination In the foam making process, the weight of the foam is always less than the sum total of the weights of the chemicals put in. This is so because during foaming, a lot of gas (in the form of C0 2 ) is given off and the entire ABA evaporates. The total gas loss consists of: carbon dioxide from the isocyanate-water-reaction evaporated ABA (e.g. Methylene Chloride) It should be noted that the amount of C02 given off is dependent on the quantity of water in the formulation. Theoretically the amount of C02 generated is equal to 2.44 times the amount of water in part by weight (p.b.w) The secondary reaction (which occurs during the curing stage) is that reaction between the hydrogen on the nitrogen atom of the Urethane and another isocyanate molecule to form an allophanate group. The reaction between water and TDI produces quite a large amount of gas (carbon dioxide) which in turn blows up (expands) the foam cells created during the reaction. Gas loss p.b.w = 2.44 x p.b.w H20 + p.b.w MC Page 3 of 7

4 Where, p.b.w is part by weight, H20 is water, and MC is Methyl Chloride. Actual foam weight (j) = Total Chemical weight gas loss. (22) Hence: Z = w P. (23) j Where, known weight is (w), Polyol Proportion is (P) and multiplying factor is (z) The actual chemical consumption (kg) is Z x Respective Chemical Proportion (24) Figure 2 (a-d) shows the DSSPPCPFP flow chart from the beginning of the production process of polyurethane form to the end. Figure 2(b): Developed DSSPPCPFP Flow chart. Figure 2(c): Developed DSSPPCPFP Flow chart. Figure 2(a): Developed DSSPPCPFP Flow chart. Page 4 of 7

5 continue without the inclusion of ABA for density below 18 kg/m3 as shown in Figure 3. Figure 3: Production possibility Alert that production cannot continue. Figure 2(d): Developed DSSPPCPFP Flow chart. Results and Discussion The developed DSSPPCPFP takes the user through series of questions and based on the values entered, it trigger the inference engine which brings about the result automatically. It analyzed the production process of polyurethane foam by giving advice to the user on decision to take, the production possibilities, the appropriate quantity and quality in other to achieve optimality. The DSSPPCPFP is so designed in a way that it advice the user on production possibility chosen whether production can take place or not. The following questions are asked from the user: Select your raw material combination for production of polyurethane. Select your value of density for production. IF: Polyol, TDI, Amine, Stannous Octate, Silicone oil, water and Density below 18 kg/m3 are selected. THEN: The Inference Engine report to the user is that the production of Polyurethane with the selected raw material combination cannot Figure 4: Production possibility Alert that production can now continue. Figure 4 is an alert from DSSPPCPFP developed that production can continue with addition of ABA into the formulation. Page 5 of 7

6 Figure 7 shows the temperature range that the chemical can be for the production to take place if good quality of foam is required. Also, Figure 8 display various types of machines and the raw cost required when 4.5 by 8 type of covered foam with weight 2000kg and density 16 kg/m3 was selected. Figure 5: Quality control module. Figure 5 display the correct proportion of chemicals needed for production of foam of weight 3000kg when density of 15 kg/m3 was selected. Figure 6 display the effect of wrong formulation on the quality of foam as selected in the quality control section of Figure 5. Figure 8: Expert system report on number of machines for production of covered foam type. Figure 6: Effect of wrong raw material formulation on quality of polyurethane foam. Validation of DSSPPCPFP Developed The validation of the DSSPPCPFP developed was done with 1000kg of Polyol and selected density of 17 kg/m3. Table 2 shows the quantity of each of the raw material needed with the corresponding gas loss and weight generated by the company Machine and the DSSPPCPFP developed. Raw Material The company quantity (kg) Quantity by DSSPPCPFP (kg) % Difference Polyol TDI Silicon Oil Amine Stannous octoate Water Figure 7: Temperature conditioning of chemical. ABA Gas Loss Page 6 of 7

7 Expected weight 1, , Table 2: Raw material formulation by the company and DSSPPCPFP Cost Method Mean Standard Deviation Standard Mean Error Manual Decision Support Table 3: Paired Sample Statistics Paired Differences Cost Method Mean Standard Standard Error mean (Uncovered) Deviation Pair 1 (Manual) And Decision Support) Table 4: Paired sample Test t D f Sig. (2- tailed) Table 3 is the t-test results for comparison between the Company cost estimate for uncovered foam and cost estimate using DSSPPCPFP developed. The result shows 2- tailed significant of in table 4, which is greater than 0.05, this implies that there is no significant difference between the manual method of costing and DSSPPCPFP developed, hence the DSSPPCPFP developed is reliable for costing of polyurethane foam but faster than the model costing method of approach, therefore save time. Conclusion A DSSPPCPFP system has been utilized to model the production planning and control processes in Polyurethane foam production. The developed model has enhanced the effectiveness of planning and the efficiency of the production through consistent and reliable quality at lower cost. The DSSPPCPFP developed advice, recommend, and provides a broad view of the production process and manufacture cost of different sizes of foam. It is evident from this study that DSS can be applied in the production process and costing of Polyurethane Foam. References 1. Jeffcat P (2006) Jeffcat Amine Catalysts for the Polyurethane Industry. 2. John BS (1990) System engineering. Prentice Hall International, Britain. 3. Blackwell H (1979) Structure of the hard segments in Polyurethane elastomers. IPC Business Press. Washington, DC, USA 4. Byran D (1999) Structure-Property Relationships of flexible Polyurethane Foams. PhD Thesis Dept. Virginia Polytechnic Institute. 5. Gomes AS (1990) Energy saving and Environmental Impact in Polyurethane foam Industry, Elsevier Applied Science, London, England. 6. Kauffman J (2002) Effect of Isocyanates on Polyol. Macmillan Publishing Co, Inc. New York, USA. 7. Raymond B (1992) Polyurethanes: A Class of modern versatile materials. J Chem Ed & Son, Inc. New York, USA. 8. Valentine C (2003) Inhibition of the Discolouration of Polyurethane Foam Caused by Ultraviolet Light. J Cellular Plastic Inc, New York, USA. Page 7 of 7