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- MakaleAçık Erişim2021Scopus SJRQ3A novel weighted mean-squared error optimization model to obtain optimal conditions of adsorption factors for a lead removal process
Özdemir, Akın; Teke, Çağatay; Serencam, Hüseyin; Uçurum, Metin; Gündoğdu, Ali
Lead (Pb) removal process from wastewater is an important issue to prevent health problems for people. For this particular purpose, a low-cost adsorbent may be beneficial for improving the adsorption capacity for the Pb removal process. The aims of this paper are four-fold. First of all, a D-optimal experimental design was selected to reduce experimental runs and its cost. Second, the effect of four adsorption design factors, stirring speed (rpm), adsorbent dosage (g), pH level, and initial metal concentration (ppm), was examined. Also, the yellow natural stone, which is from Bayburt, Turkey, was used as a cheap adsorbent for the Pb removal process from the solu-tion. Third, a novel weighted mean-squared error optimization model was developed to obtain optimal adsorption levels for adsorption factors. Besides, a verification study was conducted to verify the results of the adsorption experiment. Finally, the lead (Pb(II)) removal capacity of the yellow Bayburt stone was obtained to be 46.031 mg/g, and the results of the experiment from the proposed methodology showed a good performance for the removal study.
- MakaleYalnız künye2020A proposed response surface-based robust design model for quality engineering problems
Özdemir, Akın
The aim of robust design models is to reduce the variability reduction as small as possible. The process bias defined as a difference between the desired target value and the process mean is an important concern for quality engineering problems. In addition, the selection of different variability measures may also change optimal operating conditions for a response variable. Therefore, this paper is three-fold. One, another view of the dual response model is proposed with the three different variability measures in order to determine optimum robust design solutions for input variables while minimizing the process bias. Two, the linearization of constraints is performed using the sequential quadratic programming method as an effective optimization method. Three, a printing process from the literature is conducted to obtain the best optimal settings for input variables. Finally, the results of the proposed model show approximately % 16 more variance reduction than traditional models.
- MakaleAçık Erişim2020Bulanık İstatistiksel Proses Kontrol Tekniği ile 19x39x19 cm Bims Hafif Yapı Malzemesi Üretiminin Analizi
Uçurum, Metin; Kaplan Göztok, Kader; Özdemir, Akın
İstatistiksel Proses Kontrolü (İPK), üretim çıktılarının ürün özelliklerini belirlenen kalite aralıklarındayapılmasını kontrol eden, sağlayan ve standart dışı üretimin önüne geçmede mühendislerin kullandığı en önemlimatematik esaslı bir araçtır. Ancak konvansiyonel İPK yöntemlerinin yetersiz kaldığı durumlarda bulanık kümeteorisinden yararlanılan daha hassas istatistiksel proses kontrolüne imkân veren bulanık istatistiksel proseskontrol yaklaşımları geliştirilmiştir. Bu makalede, bir Bims fabrikası ürünü olan 19x39x19 cm Bims ürünününüretim sürecin kontrol altında olup olmadığının belirlenmesinde bulanık istatistiksel proses kontroltekniklerinden yararlanılarak elde edilen sonuçlar sunulmuştur.
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İncele - MakaleAçık Erişim2020Sürekli Kalite İyileştirme için Faktöriyel Tasarım Tabanlı Süreç Optimizasyonu
Serencam, Hüseyın; Uçurum, Metin; Özdemir, Akın
Deneyin tasarımı, yeni bir süreç geliştirmek veya mevcut bir süreci geliştirmek için kilit bir rol oynar. Literatürde sürekli kalite iyileştirme için faktöriyel deneysel tasarımları kullanılmıştır. Bu makalede, tasarım faktörlerinin optimizasyonu için bir deney verisi analizi yapmak amacıyla faktöriyel deney tasarımına sahip yeni bir yöntem sunulmaktadır. Önerilen yöntem beş ana adıma sahiptir. İlk adım deney öncesi planlama ile ilgilidir. İkinci adım faktöriyel tasarıma sahip deneysel aşamadır. Üçüncü adım bir deneye ait verileri analiz eder. Daha sonra, tasarım faktörlerinin en uygun değerlerini elde etmek için faktöriyel tasarıma dayalı optimizasyon modeli ilk defa geliştirilmiştir. Son adım deneyden elde edilen sonuçları doğrulamak için sonuçlar ve tavsiyeler adımdır. Son olarak, güncel literatürdeki sayısal bir örnek için farklı hedef değerler kullanılarak karşılaştırma çalışmaları yapılmıştır. Ayrıca, önerilen faktöriyel tasarıma dayalı süreç optimizasyon modelinin belirtilen hedef değere göre daha fazla varyansı azaltabileceği sonucuna varılmıştır.
- MakaleYalnız künye2020Scopus SJRQ1D-optimal experimental design for production models in nonstandard experiments
Özdemir, Akın
Robust design is an effective Quality by Design method to reduce product variation by selecting levels of design factors. For a number of situations, a nonstandard design region with linearly limited resources is needed to conduct an experiment. In the literature, little attention has been given to the development of robust design models for the nonstandard design region with a combination of linearly limited resources and a limited number of design points. In this paper, a selection scheme of D-optimal experimental design points is proposed to generate design points using the modified exchange algorithm for the nonstandard design region while specifying linearly limited resources and the limited number of design points. The modified exchange algorithm is able to generate global design points with less time complexity than the improved Fedorov algorithm. In addition, robust design models linking a D-optimal experimental design with quality considerations are proposed in order to obtain optimum settings of design factors for the product. Comparative studies are also presented. Finally, a real-life experimental study shows that the proposed models with the desirability function and the sequential quadratic programming technique achieve greater variance reduction than the traditional counterparts.
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İncele - MakaleAçık Erişim2019A Mixed Integer Linear Programming Model for Finding Optimum Operating Conditions of Experimental Design Variables Using Computer-Aided Optimal Experimental Designs
Özdemir, Akın
Computer-aided optimal experimental designs are an effective quality improvement tool that provides insights of information under various quality engineering problems. In the literature, considerable attention has been focused on maximizing the determinant of the information matrix in order to generate optimal design points. However, minimizing the average prediction based on the I-optimality criterion is more useful than commonly used D-optimality criterion for a number of situations. In this paper, special experimental design situations are explored where both qualitative and quantitative input variables are considered for an irregular design space with the pre-specified number of design points and the first-order polynomial model. In addition, this paper lays out the algorithmic foundations for the proposed D- and I-optimality criteria embedded mixed integer linear programming models in order to obtain optimal operating conditions using the first-order response functions. Comparative studies are also conducted. The results show that the proposed model for the D-optimality is able to identify approximately 30% smaller standard deviation value than the traditional counterpart. The proposed model for the I-optimality also provides a feasible solution whereas there is no optimum solution for the traditional counterpart.
AtıfOpenAlex5TR Dizin1İncele - MakaleAçık Erişim2019Scopus SJRQ2Modeling design parameters with taguchi experimental method for obtaining operating conditions for cu(Ii) removal through adsorption process
Serencam, Hüseyin; Özdemir, Akın; Teke, Çağatay; Uçurum, Metin
One of the environmental concerns deals with the removal process of pollutants in water and wastewater. In this paper, a removal process was used to eliminate pollutants in water and wastewater. The aim of this paper was to obtain an optimum adsorption condition for the highest metal ions adsorbed (MIA) mg/g. For this particular purpose, a Taguchi L18 design was conducted. In addition, the signal-to-noise ratio was analyzed for optimum adsorption levels. The analysis of variance was also performed to evaluate the effect of each adsorption condition on MIA (mg/g) values. Moreover, an optimization model was also proposed to find the best optimal setting of adsorption levels. Then, the confirmation tests were performed using optimum coded levels of the adsorption parameters for the verification purpose. The results of the experimental study showed a good performance that Cu(II) removal capacity was found to be 43.66 mg/g. To the best of our knowledge, this research is the first to study natural stone as an efficient, inexpensive and cheap adsorbent for the removal process.
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