Sebuah Kajian Pustaka: JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 ISSN 2541-6332 | e-ISSN 2548-4281 Journal homepage: http://ejournal.umm.ac.id/index.php/JEMMME Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 1 Thermal Design Optimization of No Phase Change Shell-and-Tube Heat Exchanger using Particle Swarm Algorithm Vera Pangni Fahriania, Reza Setiawanb, František Hrdličkac, Prihadi Setyo Darmantod a Department of Chemical Engineering, Universitas Singaperbangsa Karawang, Jl. H. S. Ronggowaluyo, Karawang, 41360, Indonesia b Department of Mechanical Engineering, Universitas Singaperbangsa Karawang, Jl. H. S. Ronggowaluyo, Karawang, 41360, Indonesia c Department of Energy Engineering, Czech Technical University in Prague, Technicka 4, Praha, 16607, Czech Republic dDepartment of Mechanical Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung, 40132, Indonesia e-mail: reza.setiawan@ft.unsika.ac.id Abstract Shell-and-tube heat exchanger is designed to satisfy certain requirements such as heat transfer capability, allowable pressure drop and limitation of size. Beside such requirements, it is important to consider economical point of view to get lowest total cost. In this study, computational program and optimization for thermal design shell-and-tube heat exchanger were built for liquid to liquid with no phase change process in four variables design parameters using Bell- Delaware method. The design variables were tube size, tube length, baffle cut to shell inside diameter ratio and central baffle spacing to shell inside diameter ratio. Particle swarm algorithm was used as optimization method to get lower solution for economical point of view shell-and-tube heat exchanger. The results from two study cases show that particle swarm algorithm got lower total cost from the original design. The total cost decreased 28.84 % in first study case and 52.57 % in second study case from the original design. Keywords: Shell-and-tube heat exchanger; minimizing cost; no phase change, particle swarm algorithm, optimization 1. INTRODUCTION Heat exchanger is important equipment in industrial process. One of their types is shell-and-tube heat exchanger which has widely used in industrial energy, petroleum industry and chemical process industry. Shell-and-tube heat exchanger is designed based on their characteristics and conditions of fluids and some design is possible to appear similarly for a particular purpose. In such design, heat transfer capability and pressure drop may similar although they have different dimension and arrangement construction. Because it is possible to get many variants design shell and tube through differences of construction, shell-and-tube heat exchangers are better to have design considering economical point of view. The design should consider total cost from investment and operational cost. The cost of investment is defined as a cost for http://ejournal.umm.ac.id/index.php/JEMMME mailto:reza.setiawan@ft.unsika.ac.id JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 2 manufacturing of shell and tube and cost of operation is defined as a cost which is needed along operational process, and it is a cost for pumping power. The design with low total cost will have a significant impact to expense for producers and users because heat exchanger commonly is used for a long time or around ten years. In the other hand, computational processes are developed rapidly and one of them is global random search methods. The uniqueness of this method can find a global optimum point in all problems of optimization. Particle swarm algorithm is adapted from natural processes. Particle Swarm Algorithm is search algorithm which is built an imitating mechanism of birds’ swarm and school of fish moving together. In additional, the method can be used easier to be implemented for iterative calculation of optimization because some supporting mathematical software can help to build algorithms. The calculation process combined with the best method and supporting software can solve the design shell-and-tube heat exchanger which has the cheapest cost. Components of heat exchanger are different depending on type shell and tube particularly. But main components of shell-and-tube heat exchangers are shell, tubes, front-end head, rear-end head and baffles as mentioned in Figure 1. Figure 1. Main parts of shell-and-tube heat exchanger [1] There are many standards of shell-and-tube heat exchanger. Some standards for shell-and-tube heat exchangers are Tubular Exchanger Manufacture Association (TEMA), Deutsches Institut für Normung (DIN), American Society of Mechanical Engineers (ASME) and other standards from Europe. However, TEMA standards are widely recognized in many producers and consumers shell-and-tube heat exchanger around the world to be used as a standard. TEMA standards are made by engineering principles, researchers and experiences in process design, manufacture, and installation to assist designer, engineers, and users to work on shell-and-tube heat exchanger. TEMA standards cover fabrication tolerances, general fabrication and performance information, installation, operation and maintenance, mechanical standards, vibration standards, thermal relations and recommended good practices [1]. 2. METHODS Procedure to design shell-and-tube heat exchanger is conducted through some steps. The step is started with input data mass flow rate and temperature both shell and tube side as well as on inlet and outlet respectively. And then calculations are executed to get overall heat transfer coefficient and pressure drops. Along calculation processes, assumption and some designer decisions are given such as assuming the value of overall heat transfer coefficient and deciding of some construction type. If the value of overall heat transfer after calculation is less than 30 % of the ratio between overall calculated JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 3 and assumption values of heat transfer while pressure drop does not exceed reasonable limits prescribed, then the design is accepted to be used. Another design may be needed if a designer considers getting a lower cost of heat exchanger. Procedure to design shell-and-tube heat exchangers are conducted through some steps. The step is started with input data mass flow rate and temperature both shell and tube side as well as on inlet and outlet respectively. And then calculations are executed to get overall heat transfer coefficient and pressure drops. Along calculation processes, assumption and some designer decisions are given such as assuming the value of overall heat transfer coefficient and deciding of some construction type. If the value of overall heat transfer after calculation is less than 30 % of the ratio between overall calculated and assumption values of heat transfer while pressure drop does not exceed reasonable limits prescribed, then the design is accepted to be used. Another design may be needed if a designer considers getting a lower cost of heat exchanger. Figure 2. Design procedure of shell-and-tube heat exchanger [2] Spesification, Design Duty, Make Energy Balance if Needed to Calculate Unspecified Flow Rates of Temperatures Collect Physical Properties Assume Value of Overall Coefficient Uo,ass Decide Number of Shell and Tube Passes, Calculate ? Tlm, Correction Factor F and ? Tm Determine Heat Transfer Area Required: Ao = q/Uo,ass ? Tm Decide Type, Tube Size, Material Layout Assign Fluids to Shell or Tube Sheet Calculation Number of Tubes Calculate Shell Diameter Estimate Tube-Side Heat Transfer Coefficient Decide Baffle Spacing and Estimate Shell-Side Heat Transfer Coefficient Calculate Overall Heat Transfer Coefficient Including Foulling Factors, Uo,cal 0 < (Uo,cal – Uo,ass)/Uo,ass < 0.3 Estimate Tube and Shell Side Pressure Drop Pressure Drop within Spesification? Estimate Cost of Exchanger Can Design be Optimazed to Reduce Cost? Accept Design Set Uo,ass = Uo,cal Yes Yes Yes No No No JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 4 The program has four variables that are tube outer diameter, tube length, baffle cut to shell inside diameter ratio and baffle spacing to shell inside diameter ratio. Bounds the program for the four variables are described in Table 1. The first bound, Tube outer diameter is taken from BWG standard which is used correspond to TEMA standard for tube size. The minimum value of tube outer diameter considers cleaning process and vibration of tubes. Cleaning process in the tube can be done with minimum tube size 0.01905 m and vibration also will be reduced using minimum tube size 0.01905 m [3]. The second bound, Range of pipe length depends on space to be expected on size shell- and-tube heat exchanger. The third bound, baffle cut to shell inside diameter ratio uses ratio the value ranging from 15 % to 45 %. It is set to support tubes mechanically against sagging and possible vibration [3]. And the fourth bound, baffle spacing to shell inside diameter ratio uses ratio the value ranging from 20 % to 80 %. Maximum TEMA standard for baffle spacing is also 80 %. It is used also to avoid failure due to tube vibration where it occurs in unsupported tube length more than 80 % [3]. Table 1. Bounds of variables Variable Minimum Value (m) Maximum Value (m) Tube outside diameter (do) 0.01905 0.051 Tube length (L) 1 10 Baffle cut to shell inside diameter ratio 0.15 0.45 Baffle spacing to shell inside diameter ratio 0.2 0.8 Figure 3. Methodology for heat exchanger optimization [1] Alternate Design: Construction Types, Flow, Arrangements, Surface Selection, etc. Designer Constraints and Design Variables Problem Specifications Including Customers Constraints and Design Variables Total Constraints and Design Variables for Optimization Problem Changed Geometry and/ or Operating Conditions Specified by Design Variables Heat Transfer and Pressure Drop Evaluation Thermophsical Properties Fixed Operating Conditions Geometrical Properties Scaled j and f Factors Objective Function and Constraints Evaluation Optimization Strategy for Redefining The Design Variables Problem Formulation Heat Exchanger Design Computer Programs Optimization Package Optimum Solution JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 5 The methodology of shell-and tube heat exchanger optimization is divided into three main parts which are problem formulation, heat exchanger design and computer program and optimization package as presented in Figure 3. Recently, it is possible to get design heat exchanger with minimum cost and satisfied on some constraints by commercial software using optimization methods. Equations for heat transfer in tube side have many forms. The equation can be selected exactly using a validity statement and Reynolds number. Some correlations heat transfer coefficient in tube side for no phase change process are expressed as follows [4]. For ( Ret Prt di L )1/3 (µ / µw) 0.14 < 2 hi = 3.66Kt / d (1) For ( Ret Prt di L )1/3 (µ / µw) 0.14 > 2 For Ret < 2100 hi = (Kt / di) 1.86 ( Ret Prt di L )1/3 (µ / µw) 0.14 (2) For 2100 < Ret < 104 hi = (Kt / di) 0.116 (Ret2/3 – 125) Prt1/3 (1 + di / L)2/3 (µ / µw) 0.14 (3) For Ret > 104 hi = (Kt / di) 0.027 Ret0.8 Prt0.4 (µ / µw) 0.14 (4) Heat transfer in shell side for no phase change is calculated using Bell-Delaware method. Calculation of Bell-Delaware method is more complex but it is accurate enough. Bell-Delaware method compares to ideal tube bank, consider leakage through leakages and bypass flows. So, calculation of Bell-Delaware method will consider correction factors. Heat transfer in shell side can be found by Eq. 5 [1]. ho = hid Jc Jl Jb Js Jr (5) Total pressure drop is the summation of pressure drop from tube and shell side. Pressure drop in tube side commonly due to frictions and indentations along tubes. Pressure drop for all tubes can be obtained by Eq. 6 [5]. ΔPt = vt 2/2 ( 4ft L di + 2.5) npρt (6) And pressure drop in shell side is calculated using Bell-Delaware method which is evaluated from cross flow tip baffle to tip baffle. Pressure drop in shell side is commonly due to dividers from baffle and frictions along flow in shell side. Pressure drop in shell side is the sum of pressure drop from the central section, window area and inlet outlet area considering some correction factors. Pressure drop in shell side can be determined by Eq. 7 [1]. ΔPs = [(Nb – 1)ΔPb,idRb + NbΔPw,id]Rl + 2ΔPb,id (1 + Nr.cw Nr.cc )RbRs (7) The estimation cost of a heat exchanger is got from the summation of investment and operational cost. Total cost can be expressed by Eq. 8 [6]. Ctot = Cinv + Cop (8) JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 6 Investment cost is used as the initial cost to make a shell-and-tube heat exchanger. It can be especially determined for shell material and tube material by Eq. 9, Eq. 10, Eq. 11, Eq. 12 or Eq. 13 [7]. For material (Shell: Carbon Steel and Tube: Carbon Steel) Cin = 6411 + 329.7A0.80 (9) For material (Shell: Carbon Steel and Tube: Stainless Steel) Cin = 7731 + 372A0.85 (10) For material (Shell: Stainless Steel and Tube: Stainless Steel) Cin = 8000 + 259.2A0.91 (11) For material (Shell: Carbon Steel and Tube: Titanium) Cin = 12821.9 + 562A0.92 (12) For material (Shell: Titanium and Tube: Titanium) Cin = 16027 + 640A0.93 (13) And the operational cost has been used for an operational process for a lifetime of a shell-and-tube heat exchanger. Actually, operational cost is used for pumping power due to pressure drop in shell and tube side. Operational cost is calculated considering inflation rate and efficiency of the pump. Operational cost due to inflation rate effects for the lifetime can be determined by Eq. 14 [8]. Cop = ∑ Co (1+ λ)k ny k=1 (14) Operational cost for annual current cost is calculated considering operation hours. It can be determined by Eq. 15 [8]. Co = P Kel τ (15) Where pumping power considering efficiency of pump can be calculated using Eq. 16 [8]. P = ( ṁt ΔPt ρt + ṁs ΔPs ρs ) 1 η (16) Process of particle swarm algorithm is started with defined initial parameters The process continues until maximum number of iteration and satisfy the criteria. Flow process for particle swarm algorithm is illustrated by flow chart in Figure 4. Particle Swarm Algorithm is search algorithm which is built an imitating mechanism of birds’ swarm and school of fish moving together. A few individuals explore to search the best position for objective function. The best position of individual in a group called global best position. The individual best position is obtained from updating initial position and velocity [9]. A flow process for Particle Swarm algorithm is illustrated by flow chart in Figure 4. After one cycle completed, the process will be repeated until a few iterations. Step by step particle swarm algorithm can be expressed in Table 4. JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 7 Figure 4. Principle Process of Particle Swarm Algorithm [4] Table 2. Step by Step Particle Swarm Algorithm [9] Step 1: Initialize imax, w, φ1, φ2, n (population size), xi,min and xi,max Step 2: Initialize the starting position and velocities of the variables as xi,k = xi,min + (xi,max – xi,min)ui k = 1 … n vi,k = 0 Step 3: Compute pi,k = f(xi,k) k = 1 … n Step 4: Compute pbesti,k = pi,k and gbesti = minimum (pbesti,k) The location of pbestk and gbest is given by pxik and gix Step 5: Update velocity vi+1,k = w1vi,k + φ1(pxik – xi,k)ui + φ2(gix – xi,k)ui Step 6: Update position xi+1,k = xi,k + vi+1,k Step 7: Update fitness pi+1,k = f(xi+1,k) Step 8: If pi+1,k < pbesti,k then pbesti+1,k = pi+1,k Step 9: Update gbesti+1 = minimum (pbesti+1,k) Step 10: If i < imax then increment i and go to step 5, else stop 3. RESULT AND DISCUSSION 3.1 First Case Study The first case study is a shell-and-tube heat exchanger with kerosene liquid in shell side and crude oil in tube side. Both shell and tube are made of stainless steel. Energy cost for shell and tube is set as 0.12 €/kWh and interest rate is set as 10 % per year. Working hour of the shell-and-tube heat exchanger is set as 7,000 hours/year and the Define Testing Function and Population Size Initialize x, v Local and Global Best Position For Each Particle Update Velocity Update Position Evaluate Function Next Particle No. Iteration = Max. No. Iteration Next Particle Solution is Global Best JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 8 lifetime of the shell-and-tube heat exchanger is set as 10 years with the efficiency of pump 0.7 [5]. Data of fluids and physical properties are known for both stream sides. The data of each stream are mass flow rate, temperature inlet and outlet, density, viscosity, thermal conductivity, specific heat and fouling resistance. The data of each stream is detailed in Table 2. Table 3. First case study: data of fluids and physical properties [5] ṁ (kg/s) Th (oC) Tc (oC) ρ (kg/m3) µ x 10 5 (Pa.s) k x 10 2 (W/mK) Cp (J/kg) Rf x 104 (m2K/W) Shell Side: Kerosene 5.52 199.0 93.3 850 40 13 2,470 61 Tube Side: Crude Oil 18.80 37.8 76.7 995 358 13 2,050 61 The original design from the first case study uses pattern of square tube arrangement, one shell pass, four tube passes, tube pitch equal to 1.25 of outer tube diameter and baffle spacing equal to 0.24 of inner diameter shell [5]. Optimization of first case study was carried out by particle swarm algorithm. Comparison of the result optimization to original data is presented in Table 3. Table 4. Design comparison of first case study to original data Parameters Original Data Particle Swarm Tube Layout (o) Square 30 N (Shell) 1 1 Np (Passes) 4 2 Nt (Tubes) 158 200 do (m) 0.025 0.01905 di (m) 0.020 0.01619 Ds (m) 0.539 0.399 Pt (m) 0.031 0.02381 Lbc (m) 0.127 0.129 Lc (m) - 0.063 L (m) 5.983 5.300 A (m2) 74.21 63.60 ΔTlm (K) 84.55 84.55 F 0.89 0.89 vt (m/s) 1.523 0.915 vs (m/s) 0.483 0.639 Gt (kg/m2s) 1,515.4 910.5 Gs (kg/m2s) 410.6 543.2 Prt 5.6 5.6 Prs 7.6 7.6 Ret 8,468 41,184 Res 25,344 25,870 Q (W) 1,441,156 1,441,156 hi (W/m2K) 1,086 2,130.4 ho (W/m2K) 978.9 759.1 U (W/m2K) 268.1 312.8 ΔPt (Pa) 53,195 8,057 ΔPs (Pa) 25,344 18,026 P (W) 1,671 385 JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 9 Parameters Original Data Particle Swarm Cin (€) 21,054 19,344 Cop (€) 8,920 1,986 Ctot (€) 29,974 21,330 Optimization process using particle swarm algorithm has been successfully minimizing total cost of shell-and-tube heat exchanger on the first case study. Algorithm methods have been decreasing total cost of the shell-and-tube heat exchanger 28.84 % using Particle Swarm Algorithm from the total cost of original data, as mentioned in Table 7. Total cost decreases on first case study due to decreasing total investment and operation cost. In this case, total operational cost decreases 77.74 % using Particle Swarm Algorithm. Total investment cost decreases 8.12 % using Particle Swarm algorithm from total operational cost and total investment cost of original data. Graphic 1. Cost comparison of first case study Graphic 2. Heat transfer coefficient comparison of first case study 21.054 8.920 29.974 19.344 1.986 21.330 0 5.000 10.000 15.000 20.000 25.000 30.000 35.000 Investment Cost Operation Cost Total Cost C o s t (€ ) First Case Study: Kerosene - Crude Oil Original Data Particle Swarm 1.230 6.186 9.779 948 2.385 4.437 0 2.000 4.000 6.000 8.000 10.000 12.000 Overall Shell Side Tube Side H e a t T ra n s fe r C o e ff ic ie n t (W /m 2 K ) First Case Study: Kerosene - Crude Oil Original Data Particle Swarm https://id.wikipedia.org/wiki/Simbol_euro https://id.wikipedia.org/wiki/Simbol_euro https://id.wikipedia.org/wiki/Simbol_euro JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 10 Value of overall, tube side and shell side heat transfer coefficient tend higher than original data. Results of overall heat transfer coefficient increases 16.66 % using Particle Swarm algorithm from overall heat transfer of the original data. For heat transfer in tube side, the results increase 96.17 % using Partical Swarm algorithm from original data. For heat transfer in shell side, the results increase 22.45 % using Particle Swarm algorithm from the original data. Overall heat transfer increases compared to original data because heat transfer area is smaller than the original data. It affects increasing value of heat transfer coefficient in shell and tube side, as presented in Graphic 2. Pressure drop tends to decrease in the tube side. Pressure drop in tube side decreases 84.85 % using Particle Swarm algorithm from the original data. Pressure drops in shell side decreases 28.87 % using Particle Swarm algorithm from original data. As appears in Graphic 3, It happens because velocity both in tube and shell side is decrease. Graphic 3. Pressure drop comparison of first case study 3.2 Second Case Study The second case study is a shell-and-tube heat exchanger with distilled water in shell side and raw water in tube side. Both shell and tube are made of stainless steel. Energy cost for shell and tube is set as 0.12 €/kWh and interest rate is set as 10 % per year. Working hour of the shell-and-tube heat exchanger is set as 7,000 hours/year and the lifetime of the shell-and-tube heat exchanger is set as 10 years with the efficiency of pump 0.7 [5]. Data of fluids and physical properties are known from both stream sides. The data of each stream are mass flow rate, temperature inlet and outlet, density, viscosity, thermal conductivity, specific heat and fouling resistance. The data of each stream is detailed in Table 4. Table 5. Second case study: data of fluids and physical properties [5] ṁ (kg/s) Th (oC) Tc (oC) ρ (kg/m3) µ x 10 5 (Pa.s) k x 10 2 (W/mK) Cp (J/kg) Rf x 104 (m2K/W) Shell Side: Distilled Water 22.07 33.9 29.4 995 80 62 4,180 17 Tube Side: Raw Water 35.31 23.9 26.7 999 92 62 4,180 17 25.355 53.195 18.026 8.057 0 10.000 20.000 30.000 40.000 50.000 60.000 Shell Side Tube Side P re s s u re D ro p ( P a ) First Case Study: Kerosene - Crude Oil Original Data Particle Swarm JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 11 Optimization of second case study was carried out by genetic algorithm. Comparison of the result optimization to original data is presented in Table 5. Table 6. Design comparison of second case study to original data Parameters Original Data Particle Swarm Tube Layout (o) Triangular 30 N (Shell) 1 1 Np (Passes) 2 2 Nt (Tubes) 160 432 do (m) 0.019 0.01905 di (m) 0.0152 0.01619 Ds (m) 0.387 0.564 Pt (m) 0.023 0.02381 Lbc (m) 0.305 0.341 Lc (m) 5.904 2.822 L (m) - 0.086 A (m2) 56.35 73.09 ΔTlm (K) 6.31 6.31 F 0.94 0.94 vt (m/s) 2.436 0.793 vs (m/s) 1.022 0.578 Gt (kg/m2s) 2,433.6 792.5 Gs (kg/m2s) 1,016.9 575.7 Prt 6.2 6.2 Prs 5.4 5.4 Ret 40,207 13,948 Res 17,155 13,709 Q (W) 415,137 415,137 hi (W/m2K) 9,799 4,437.0 ho (W/m2K) 6,186 2,385.1 U (W/m2K) 1,230 948.4 ΔPt (Pa) 65,657 4,708 ΔPs (Pa) 88,520 9,141 P (W) 6,120 527 Cin (€) 18,162 20,875 Cop (€) 31,589 2,722 Ctot (€) 49,751 23,597 Optimization process using particle swarm has been successfully minimizing total cost of shell-and-tube heat exchanger on the first case study. Algorithm methods have been decreasing total cost of the shell-and-tube heat exchanger 52.57 % using particle swarm algorithm from the total cost of original data, as mentioned in Table 9. Total cost decreases on first case study due to decreasing total investment and operation cost. In this case, total operational cost decreases 91.38 % using particle swarm algorithm. Total investment cost increases 14.94 % using particle swarm algorithm from total operational cost and total investment cost of original data. https://id.wikipedia.org/wiki/Simbol_euro https://id.wikipedia.org/wiki/Simbol_euro https://id.wikipedia.org/wiki/Simbol_euro JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 12 Graphic 4. Cost comparison of second case study Value of overall, tube side and shell side heat transfer coefficient tend to lower than original data. Results of overall heat transfer coefficient decreases 22.90 % using particle swarm algorithm from overall heat transfer of the original data. For heat transfer in tube side, the results decrease 54.72 % using particle swarm algorithm from original data. For heat transfer in shell side, the results decrease 61.44 % using particle swarm algorithm from the original data. Overall heat transfer decreases compared to original data because heat transfer area is smaller than the original data. It affects increasing value of heat transfer coefficient in shell and tube side, as presented in Graphic 4. Graphic 5. Heat transfer coefficient comparison of second case study Pressure drop tends to decrease in the tube side. Pressure drop in tube side decreases 92.83 % using particle swarm algorithm from the original data. Pressure drops in shell side decreases 88.87 % using Genetic algorithm. As appears in Graphic 5, It happens because velocity both in tube and shell side is decrease. 18.162 31.589 49.751 20.875 2.722 23.597 0 10.000 20.000 30.000 40.000 50.000 60.000 Investment Cost Operation Cost Total Cost C o s t (€ ) Second Case Study: Distilled Water - Raw Water Original Data Particle Swarm 268 979 1.086 313 759 2.130 0 500 1.000 1.500 2.000 2.500 Overall Shell Side Tube Side H e a t T ra n s fe r C o e ff ic ie n t (W /m 2 K ) Second Case Study: Distilled Water - Raw Water Original Data Particle Swarm JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 13 Graphic 6. Pressure drop comparison of second case study The authors’ manuscripts should be completed with title, abstract, keywords and the main text. Furthermore, the authors should present tables, figures, and equations in good order. 4. CONCLUSION Based on the research that has been done, some conclusions can be drawn as follows. Collecting suitable equations for the computational process have been done, calculation process does not need any table, chart or graph to define parameters to design of shell-and-tube heat exchangers. All calculation process were done by using the formula for wide range cases. It could be design shell-and-tube heat exchangers tube layout 30o, 45o, and 90o. The estimate cost is provided for shell-and-tube heat exchanger made of carbon steel, stainless steel, titanium and the combination of their materials. Building codes of efficient algorithm for computational calculation and correspond to TEMA standards has been done, sequences algorithm in computational process were work properly and define TEMA standards into algorithm such as BWG tube standard, minimum value of 1.25 tube pitch to outer tube diameter ratio and maximum value 80 % baffle spacing to shell inside diameter ratio. Setting parameters for particle swarm algorithm have been found to get a minimum total cost of shell-and-tube heat exchangers. in particle swarm algorithm, minimum cost can be got using particles 250, first tuning factor 2, second turning factor 2, and 100 maximum iterations. The program has been applied for solving three thermal design shell-and-tube heat exchangers. The first case is a shell-and-tube heat exchanger with kerosene and crude oil fluids, the results show that program can reduce 28.84 % using particle swarm algorithm of the total cost from the original data. The second case is a shell-and-tube heat exchanger with distilled water and raw water, in which the result shows that program can reduce 52.57 % using particle swarm algorithm of the total cost from the original data. 88.520 65.657 9.141 4.708 0 10.000 20.000 30.000 40.000 50.000 60.000 70.000 80.000 90.000 100.000 Shell Side Tube Side P re s s u re D ro p ( P a ) Second Case Study: Distilled Water - Raw Water Original Data Particle Swarm JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6, No. 1, 2021 doi: 10.22219/jemmme.v6i1.11766 Setiawan | Thermal Design Optimization of No Phase Change Shell-and-Tube Heat ... 14 REFERENCES 1. Shah, R. K. and Sekulic, D. P. Fundamentals of Heat Exchanger Design. New Jersey: John Wiley & Sons, Inc. 2003. 2. Sinnot, R. K. Chemical Engineering Design. Oxford: Elsevier Butterwoth-Heinneman. 2005. 3. Hewitt, G. F., Shires G. L., and Bott, T. R. Process Heat Transfer. New York: Begell House, Inc. 2000. 4. Sadeghzaden, H., Ehyaei M. A., Rosen, M. A. Techno-Economic Optimization of a Shell and Tube Heat Exchanger by Genetic and Particle Swarm Algorithm. Energy Conversion and Management, vol. 93, pp. 84-91, 2015. https://doi.org/10.1016/j.enconman.2015.01.007 5. Yang, J., Oh, S., Liu, W. Optimization of Shell-and-Tube Heat Exchangers using A General Design Approach Motivated by Constructal Theory. International Journal of Heat and Mass Transfer, vol. 77, pp. 1144-1154, 2014. https://doi.org/ 10.1016/j.ijheatmasstransfer.2014.06.046 6. Caputo, A. C., Pelagagge, P. M., Salini, P. Heat Exchanger Design Based on Economic Optimization. Applied Thermal Engineering, vol. 28, pp. 1151-1159, 2008. https://doi.org/10.1016/j.applthermaleng.2007.08.010 7. Taal, M., et al. Cost Estimation and Energy Price Forecasts for Economic Evaluation of Retrofit Projects. Applied Thermal Engineering, vol. 23, pp. 1819-1835, 2013. https://doi.org/ 10.1016/S1359-4311(03)00136-4 8. Sanaye, S., Hajabdollahi, H. Multi-Objective Optimization of Shell and Tube Heat Exchangers. Applied Thermal Engineering, vol. 30, pp. 1937-1945, 2010. https://doi.org/10.1016/j.applthermaleng.2010.04.018 9. Arora, R. K. Optimization Algorithms and Applications. Florida: CRC Press Taylor & Francis Group. 2015. https://doi.org/10.1016/j.enconman.2015.01.007 http://dx.doi.org/10.1016%2Fj.ijheatmasstransfer.2014.06.046 http://dx.doi.org/10.1016%2Fj.ijheatmasstransfer.2014.06.046 https://doi.org/10.1016/j.applthermaleng.2007.08.010 http://dx.doi.org/10.1016%2FS1359-4311(03)00136-4 https://doi.org/10.1016/j.applthermaleng.2010.04.018