USABILITY OF CONTROL CHARTS TO MONITOR VARIATION OF QUALITY PARAMETERS IN COAL-FIRED THERMAL POWER PLANTS

Madencilik, 2020, 59(4), 247-253 Mining, 2020, 59(4), 247-253 Orijinal Araştırma / Original Research USABILITY OF CONTROL CHARTS TO MONITOR VARIATION OF QUALITY PARAMETERS IN COAL-FIRED THERMAL POWER PLANTS KÖMÜR YAKITLI TERMİK SANTRALLERDE KALİTE PARAMETRELERİNİN DEĞİŞİMİNİN İZLENMESİ İÇİN KONTROL GRAFİKLERİNİN KULLANILABİLİRLİĞİ Ali Can Özdemira,* a Çukurova University, Engineering Faculty, Mining Engineering Department, Adana, TURKEY Geliş Tarihi / Received : 16 Haziran / June 2020 Kabul Tarihi / Accepted : 2 Ekim / October 2020 ABSTRACT Keywords: Statistical process control, Control charts, Calorific value, Unit power. During the production of electrical energy from coal-fired thermal power plants, calorific and unit power values are the most important indicators for evaluating the productivity of the process. These values are measured periodically, and the resulting measurements are monitored to detect root causes of variation that may occur in production process. As this application is currently performed by manual methods, the probability of obtaining incorrect results is quite high. This study aims to statistically analyze process control on the variation of quality parameters and detect root causes of unusual variations using Shewhart and cumulative sum control charts. For this purpose, the usability of these control charts was tested on Afşin-Elbistan B thermal power plant. As a result, these charts identified fluctuations in the efficiency of generating electrical energy and unusual variations in the process. Furthermore, it is recommended that these control charts could be developed and applied in similar type of process. ÖZ Anahtar Sözcükler: Enerji verimliliği, İstatistiksel proses kontrol, Kontrol grafikleri, Kalori değeri, Ünite gücü. Kömür yakıtlı termik santrallerden elektrik enerjisi üretimi sırasında prosesin verimliliğini değerlendirmek için kalorifik değer ve birim güç değeri en önemli parametrelerdir. Bu değerler periyodik olarak ölçülür ve ölçüm sonuçları üretim sürecinde ortaya çıkabilecek dalgalanmaların temel nedenlerini tespit etmek için izlenir. Bu uygulama mevcut durumda manuel yöntemlerle gerçekleştirildiğinden, hatalı sonuçların elde edilme olasılığı oldukça yüksektir. Bu çalışma, Shewhart ve kümülatif toplam kontrol grafiklerini kullanarak kalite parametrelerinin değişimi üzerindeki proses kontrolünü istatistiksel olarak analiz etmeyi ve olağandışı dalgalanmaların temel nedenlerini tespit etmeyi amaçlamaktadır. Bu amaçla, bu kontrol grafiklerinin kullanılabilirliği Afşin-Elbistan B termik santrali üzerinde test edilmiştir. Sonuç olarak, bu grafikler kullanılarak elektrik enerjisi üretme verimliliğindeki dalgalanmaları ve süreçteki olağandışı değişimler belirlenmiştir. Ayrıca, bu kontrol grafiklerinin geliştirilmesi ve benzer prosesler için de uygulanması önerilmektedir. * Sorumlu yazar / Corresponding author: / https://orcid.org/0000-0003-3064-4264 247 A.C.Özdemir / Scientific Mining Journal, 2020, 59(4), 247-253 INTRODUCTION Monitoring the production process of coalfired thermal power plants is crucial in many industries. To ensure stable production improving INTRODUCTION the performance of the process and reducing the variability in critical quality parameters are Monitoring the production process of coal-fired necessary. Statistical process control (SPC) thermal power plants is crucial in many industries. methodstable has been developed improving to accomplishthe this To ensure production goal. The charts areand powerful, effectivethe and performance of control the process reducing important tools for the SPC method. These are variability in critical quality parameters are generally used to detect unusual variation in necessary. Statistical process control (SPC)the process andto to monitor the industrial method manufacturing has been developed accomplish this processes (Guo and Dunne, 2006; Noorossana goal. The control charts are powerful, effective and andtools Vaghefi, Montgomery, 2009; Aldosari important for 2006; the SPC method. These are et al., 2018). generally used to detect unusual variation in the manufacturing process and to developed monitor the Walter A. Shewhart has theindustrial concept of processes (Guo and Dunne, 2006; Noorossana statistical control chart (Shewhart, 1924). Presently, and Vaghefi, 2006; isMontgomery, et of this concept known as the2009; formalAldosari beginning al., 2018). SPC (Montgomery, 2009). Recently, new statistical control charts have been developed along with the Walter A. Shewhart has charts. developed of classical Shewhart Thesethe areconcept exponentially statistical control chart (Shewhart, 1924). Presently, weighted moving average (EWMA), adaptive this concept known as cumulative the formal sum beginning of EWMAis (AEWMA), (CUSUM), SPC (Montgomery, 2009). Recently, new statistical adaptive CUSUM (ACUSUM), double sampling control charts have been developed with(SPRT) the (DS) and sequential probability along ratio test classicalcontrol Shewhart These exponentially chartscharts. (Ou et al., 2012;are Haq, 2018). weighted moving average (EWMA), adaptive chartscumulative are defined graphical EWMA Control (AEWMA), sum as (CUSUM), representations of the change in time of the quality adaptive CUSUM (ACUSUM), double sampling parameter that has been measured or calculated (DS) and sequential probability ratio test (SPRT) from a sample in the process (Montgomery, 2009). control charts (Ou et al., 2012; Haq, 2018). The main purpose of control charts is to monitor the determine the reasons Control process chartsand are defined as affecting graphicalthe process stability by visually defining the behavior representations of the change in time of the quality of critical quality parameters (Yerel et al., 2007; parameter that has been measured or calculated Hachichainand Abbas et 2009). al. 2013; from a sample the Ghorbel, process 2012; (Montgomery, Deniz and Umucu 2013; Alcantara et al., 2017). The main purpose of control charts is to monitor These charts contain three horizontal lines: upper the process and determine the reasons affecting control limit (UCL), control limit (CL) and lower the process stability by visually defining the control limit (LCL). CL is the line representing behavior of critical quality parameters (Yerel et al.,the average ofand the Ghorbel, process, LCL UCL, et located 2007; Hachicha 2012;and Abbas al. below and above the average line, respectively, 2013, Deniz and Umucu 2013, Alcantara et al., are the charts lines representing the horizontal control limitslines: of the 2017). These contain three process. If a plotted statistic is between the control upper control limit (UCL), control limit (CL) and limits, then indicates theline process is in control lower control limit it(LCL). CLthat is the representing and no is required. if aUCL, plotted statistic the average of action the process, LCLBut and located is outside the control limits, then it indicates tha (...truncated)