Fuzzy Logic Based Ventilation for Controlling Harmful Gases in Livestock Houses

Transcription

1 Fuzzy Logc Based Ventlaton for Controllng Harmful Gases n Lvestock Houses Nur Caglayan 1,*,H. Kursat Celk 2, Allan E.W. Renne 3 1 Dept. of Mechactronc Engneerng, Faculty of Engneerng, Akdenz Unversty, Antalya, TURKEY 2 Dept. of Agrcultural Machnery, Faculty of Agrculture, Akdenz Unversty, Antalya, TURKEY 3 Lancaster Product Development Unt, Engneerng Dept., Lancaster Unversty, Lancaster, UNITED KINGDOM *Phone: , Fax: , E-mal: nurcaglayan@akdenz.edu.tr 1 Abstract There are many factors that nfluence the health and productvty of the anmals n lvestock producton felds, ncludng temperature, humdty, carbon doxde (CO2), ammona (NH3), hydrogen sulfde (H2S), physcal actvty and partculate matter. Hgh NH3 concentratons reduce feed consumpton and cause daly weght gan. At hgh concentratons, H2S causes respratory problems and CO2, dsplace oxygen, whch can cause suffocaton or asphyxaton. Good ar qualty n lvestock facltes can have an mpact on the health and well-beng of anmals and humans. Ar qualty assessment s bascally depend on strctly gven lmts wthout takng nto account specfc local condtons between harmful gases and other meteorologcal factors. The stated lmtatons may be elmnated. usng controllng systems based on neural networks and fuzzy logc. Ths paper descrbes a fuzzy logc based ventlaton algorthm, whch can calculate dfferent fan speeds under pre-defned boundary condtons, for removng harmful gases from the producton envronment. In the paper, a novel fuzzy logc model has been developed based on a Mamedan s fuzzy method. The model has been bult on MATLAB software. As the result, optmum fan speeds under pre-defned boundary condtons have been presented. Keywords ar qualty, fuzzy logc model, lvestock housng, fan speed I. INTRODUCTION There are many mplcatons and potental mpacts of ar qualty on human and anmal health. Extensve research documents acute and chronc respratory dsease and dysfuncton among workers, anmal 2 s n lvestock and poultry buldngs due to partculate and gaseous pollutants. Mantanng good ar qualty s not only mportant for the productvty of the anmals, but also for the welfare of the anmals. Ar qualty s also a concern to producers as well as rural resdents. Confned Feedng Operatons (CFOs) lvestock ar qualty may have a regonal, natonal and global mpact on the envronment. CFOs can affect ar qualty through emssons of gases (ammona and hydrogen sulfde), partculate matter, volatle organc and odour. Especally, hgher concentratons of ammona (NH 3) and carbon doxde (CO 2) n anmal barns can negatvely affect producton and health of anmals and workers. Ammona effects human and anmal health n both gas and partcle paheses. The partculate form of ammona has broader mplcatons for the publc, whereas the gaseous form s a localzed concern for the health of anmals and agrcultural workers. Ammona gas s a hghly hydrophlc base that has rrtant propertes when nhaled and when t s combned wth water, t can njure and burn the respratory tract [1]. The base form of ammona, ammonum hydroxde, dssolves n the water of mucus membranes, hydrolyzes, and rapdly rrtates tssues due to the hgh ph that results [2]. Ammona can also alter the uptake of oxygen by hemoglobn due to the ncrease of ph wthn the blood, whch leads to decreased oxygenaton of tssues, and decreased metabolc functon. Typcal ammona levels n well-ventlated, envronmentally regulated buldngs are ppm wth lqud manure systems and 50 ppm where manure and urne are deposted on sold floors. Due to the sde effects of ammona gas exposure over 25 ppm (17 mg.m - 3 ), the Amercan Conference of Governmental Industral Hygensts (ACGIH) recommends 8-hour maxmum exposure lmt of 25 ppm to have protecton aganst the chronc effects of ammona exposure [3]. A 15 mn short-term exposure lmt of 35 ppm (24 mg.m -3 ) has been establshed by ACGIH and t has also been adopted by OSHA to reduce rrtant effects of ammona exposure (.e. eye and upper respratory tract rrtaton). However, due to possble cumulatve health effects over tme, the recommended daly long-term occupatonal exposure lmt of ammona for agrcultural workers s 7 ppm [3], and 300 parts per bllon (ppb) for communty exposure (communty exposure must be strcter because communtes contan very susceptble people such as the elderly and chldren) [4]. At moderate concentratons (50 to 150 ppm), ammona exposure can lead to eye, throat and skn rrtaton as well as cough and mucus buld up. Prolonged exposure at ths level can result n the transfuson of ammona from the alveol nto the bloodstream and a subsequent dsrupton of oxygen uptake by hemoglobn. At hgh concentratons (>150 ppm) ammona can scar lung tssue, cause lower lung nflammaton and pulmonary edema. Exposure to hgh concentratons of ammona (500 to 5000 ppm) wll cause death n a relatvely short tme perod from preventon of oxygen uptake by hemoglobn [5]. These levels are rarely found near lvestock operatons, but t may occur n closed manure storage facltes and poorly ventlated buldngs where ammona concentratons can accumulate. In general, an optmal thermal envronment s defned for each speces n terms of ts effects on producton:

2 there are a few specfc recommendatons n terms of dsease. Several authors have dscussed and reported the effects of temperature on the mlk yeld of dary cows [6] and the egg yeld of layng hens [7]. The lower crtcal temperature defnes the lower lmt of the range of optmal temperature: the upper lmt s gven by the upper crtcal temperature. The lower crtcal temperature s affected by factors such as age, sex, breed [8], food energy level and ntake, featherng [9], stockng densty [10], beddng system, etc. The CIGR commsson [11], recommended maxmum and mnmum values of RH as a functon of ndoor temperature, for example, an RH of 50-90% at 0 C followed by a steady decrease of RH to a tolerable range of 40-60% at 30 C. The fuzzy logc control s one of the most useful approaches for utlzng the qualtatve knowledge of a system to desgn a controller. Fuzzy logc control s generally applcable to plants that are mathematcally poorly modelled and where the qualtatve knowledge of experenced operators s avalable for provdng qualtatve control. On the bass of ths dea, some fuzzy models based fuzzy control system desgn methods have appeared n the fuzzy control feld [12]. Ths paper descrbes a fuzzy logc based automatc fan speed control for optmum condtons requred for a lvestock house ventlaton consderng optmum temperature, humdty, CO 2, NH 3, and H 2S parameters. The fuzzy Logc Toolbox n MATLAB software has been used to develop ths algorthm. In the algorthm, fve nputs and sngle output have been consdered. As the result, optmum fan speeds under pre-defned boundary condtons have been presented. II. MATERIALS AND METHODS Fuzzy control nput and output values were defned n three lngustc expressons as Low (L), Medum (M) and Hgh (H). Fve parameters were chosen as nput paramters (temperature, humdty, CO 2, NH 3, H 2S) and sngle parameter was chosen as output speed of exhaust fan. The features and fuzzy lngustcs operatons of nput/output system varants are gven n Table 1. A fuzzy logc system (FLS) can be defned as the nonlnear mappng of an nput data set to a scalar output data. A FLS conssts of four man parts: fuzzfer, rules, nference engne and defuzzfer. These components and the general archtecture of a FLS are shown n Fg. 1 [13]. The process of fuzzy logc was explaned n Algorthm: Frstly, a crsp set of nput data are gathered and converted to a fuzzy set usng fuzzy lngustc varables, fuzzy lngustc terms and membershp functons. Ths step s known as fuzzfcaton. Afterwards, an nference wass made based on a set of rules. Lastly, the resultng fuzzy output was mapped to a crsp output usng the membershp functons, n the defuzzfcaton step. TABLE I. THE FUZZY LINGUISTIC EXPRESSIONS OF SYSTEM VARIANTS Parameter Lngustc Expresson Mn. Max. Temperature ( C) Low, Medum, Hgh Humdty (%) CO 2 (ppm) NH 3 (ppm) H 2 S (ppm) Speed of fan (mn -1 ) (output) Low, Medum, Hgh Low, Medum, Hgh Low, Medum, Hgh 5 25 Low, Medum, Hgh 0 10 Low, Medum, Hgh Fgure 1. Fuzzy Logc System (FLS). In ths study, Mamdan fuzzy system was used for the llustraton whch uses Center of Gravty for Sngletons algorthm (COG) for defuzzfcaton (1) [14]. COG = =1 p (1) Where u s output varable, p s number of sngletons, µ s membershp functon after accumulaton, s ndex. The FIS (Fuzzy Inference System) Edtor n Fuzzy Logc Toolbox Graphcal User Interface Tools defnes the Fuzzy Base Class, fve nputs whch are Temperature, relatve humdty (RH), CO 2, NH 3, H 2S and one output whch s Fan Speed (Fg. 2). To control the nsde values, the fuzzy controller reads the nsde temperature, humdty, CO 2, NH 3, H 2S after every samplng perod and after that they are calculated. The fuzzy varables are represented n Fgures 3-8 respectvely. The fuzzy output sgnal wth a range of 0 to 2000 has three membershp functons whch are "Low", "Medum", and "Hgh" (Fg. 7). =1 u. Fgure 2. FIS Edtor p

3 Fgure 3. Membershp of fuzzy set n temperature Fgure 7. Membershp of fuzzy set n H 2S Fgure 4. Membershp of fuzzy set n humdty Fgure 5. Membershp of fuzzy set n CO 2 Fgure 6. Membershp of fuzzy set n NH 3 III. Fgure 8. The fuzzy output RESULTS AND DISCUSSION Fuzzy rules were desgned manually. Related rules were generated for all combnatons of selected nput varable and consequent fuzzy terms was opertaed by the Rule Edtor. These rule sets were put n a form accordng to nput paramters and output paramters were obtaned. The fuzzy rule sets of ths desgned fuzzy system nclude 243 rules are shown n Table 2. The Rule Vewer shown n Fg. 9 s representon of the fuzzy member functons accordng to nput values of the smulaton. Every rule can be deactvated and ndependent rule weght can be defned for each consequent varable. The rules were defned by selectng the rght sequence n the IF-THEN sequence. The example of fuzzy rule s: "IF Temperature s Hgh AND Humdty s Medum AND CO 2 s Low AND NH 3 s Medum AND H 2S s Hgh THEN Fan Speed s Hgh". The MATLAB fuzzy logc toolbox was used for fuzzy ar condton system desgn. The sgnal value of temperature (18 C), relatve humdty (55%), CO 2 (1500 ppm), NH 3 (15 ppm) and H 2S (5 ppm) were consdered to desgn the proposed ar condtonng system. The sgnal values of pre-defned envronment factors were calculated as fuzzy varables. At the defuzzfcaton process, eght nput parameter values from the nference engne whch are R 014, R 023, R 041, R 050, R 095, R 104, R 122, R 131 have been determned. At the end of the solve process, defuzzfed output sgnal of "Fan Speed" were calculated (Table 2).

4 TABLE 2. SINGLETON VALUES FOR THE FUZZY OUTPUT Rules Inputs O utput Temp. RH C0 2 NH 3 H 2 S (mn -1 ) 14 L L M M M M 23 L L H M M H 41 L M M M M M 50 L M H M M H 95 M L M M M M 104 M L H M M H 122 M M M M M M 131 M M H M M H 1160 (L: Low; M: Medum; H: Hgh) Fgure 10. Temperature Humdty Fgure 11. Temperature - CO 2 Fgure 9. Fuzzy rule vewer Fgure 12. Temperature - NH 3 The followng Table 3 demonstrates 16 test pars to valdate the fuzzy model as well as collect qualtatve nformaton on t. Addtonally, a smulaton graph was obtaned accordng to the test outputs (Fgures 10-13). The graph was generated through the Surface Vewer the Fuzzy Logc Toolbox. Fgure 13. Temperature - H 2S

5 TABLE 3. TEST OUTPUTS OF FUZZY MODEL Smulaton Temp. RH CO 2 NH 3 H 2 S Fan Speed Scenaro No ( C) (%) (ppm) (ppm) (ppm) (mn -1 ) IV. CONCLUSIONS In ths paper, a novel algorthm was descrbed for fuzzy ar condton controller system n lvestock buldngs. Relatonshps between physcal varables whch are effected on ventlaton systems can be defned and complex ventlaton can be succesfully operated thruough developed fuzzy logc approaches. Intutve knowledge of nput and output parameters would be enough to desgn an optmally performng system. Specfc to ths study some output (fan speed) paramters can be hghlghted as folowngs; In the smulaton scenaro no: 6 (Table 3), the fan speed was calculated as 1160 mn -1 (n the range of medum ). In the smulaton scenaro no: 15 (Table 3), the fan speed was calculated as 1620 mn -1 (n the range of hgh ). the smulaton scenaro no: 7, 8 and 16 (Table 3), the fan speed was calculated as 1670 mn -1 (n the range of hgh ). Ths study also extracted that temperature and humty parameters were the most effected nput paramters on the fan speed than the others.as a chemcal compound NH 3 paramters were also found as the mportant parameter on the fan speed. REFERENCES [1] S. S. Issley and E. Lang. (February 2008). Ammona toxcty. E-Medcne [onlne]. Avalable: [2] S. S. Zumdahl, Chemstry. 4th ed. Houghton Mffln CO, Boston, NY, 1997, [3] S. W. Gay, Ammona Emssons and Anmal Agrculture, Vrgna Cooperatve Extenson, Puplcaton , Bologcal Systems Engneerng College of Agrculture and Lfe Scences, Vrgna Polytechnc Insttute and State Unversty, Vrgna State, Petersburg, USA, [4] K. J. Donham, D. Cumro, S. J. Reynolds and J. A. Merchant. Dose-response relatonshps between occupatonal aerosol exposures and cross-shft declnes of lung functon n poultry workers: recommendatons for exposure lmts, J. Occup. Envron. Med. Vol. 42, pp , [5] J. A. Merchant, J. Klne, K. J. Donham, D. S. Bundy, and C. J. Hodne, Human health effects, In Iowa concentrated anmal feedng operaton ar qualty study. Unversty of Iowa, Ames, Iowa. pp , [6] A. J. F. Webster, Optmal housng crtera for rumnants. In: Envronmental Aspects of Housng for Anmal Producton, Clark J A, ed., London: Butterworths, pp , [7] D. R. Charles, A model of egg producton, Brtsh Poultry Scence, vol. 25, pp , [8] A. M. Henken, H. A. Brandsma, W. V. D. Hel, M. W. A. Verstegen, Heat balance characterstcs of lmt-fed growng pgs of several breeds kept n groups at and below thermal neutralty. Journal of Anmal Scence vol. 69, pp , [9] M. G. Macleod, Factors nfluencng the agreement between thermal physology measurements and feld performance n poultry, Archve of Expermental Veternary Medcne, vol. 38, pp , [10] A. Burmester, M. Jurkschat, M. Nchelmann, Influence of stockng densty on the heat balance n the domestc fowl (Gallus domestcus), Journal of Thermal Bology, vol. 11, pp , [11] CIGR Clmatsaton of anmal houses. Report of workng group, Scottsh Farm Buldng Investgaton Unt, Crabstone, Aberdeen, Scotland, [12] H. O. Wang, K. Tanaka, and M. F. Grffn, An approach to fuzzy control of nonlnear systems: Stablty and desgn ssues, IEEE Trans. Fuzzy Syst., vol. 4, pp , [13] J. Mendel, Fuzzy logc systems for engneerng: a tutoral, Proceedngs of the IEEE, vol. 83(3), pp , [14] E. H. Mamdan, S. Asslon, An Experment n Lngustc Synthess wth a Fuzzy Logc Controller, Intl J. Man- Machne Stud. Vol.7, pp. 1-13, 1974.