PMID- 27951498
OWN - NLM
STAT- MEDLINE
DCOM- 20181005
LR  - 20191210
IS  - 1873-3336 (Electronic)
IS  - 0304-3894 (Linking)
VI  - 325
DP  - 2017 Mar 5
TI  - A prediction model of ammonia emission from a fattening pig room based on the 
      indoor concentration using adaptive neuro fuzzy inference system.
PG  - 301-309
LID - S0304-3894(16)31139-6 [pii]
LID - 10.1016/j.jhazmat.2016.12.010 [doi]
AB  - Ammonia (NH(3)) is considered one of the significant pollutions contributor to 
      indoor air quality and odor gas emission from swine house because of the negative 
      impact on the health of pigs, the workers and local environment. Prediction 
      models could provide a reasonable way for pig industries and environment 
      regulatory to determine environment control strategies and give an effective 
      method to evaluate the air quality. The adaptive neuro fuzzy inference system 
      (ANFIS) simulates human's vague thinking manner to solve the ambiguity and 
      nonlinear problems which are difficult to be processed by conventional 
      mathematics. Five kinds of membership functions were used to build a well fitted 
      ANFIS prediction model. It was shown that the prediction model with "Gbell" 
      membership function had the best capabilities among those five kinds of 
      membership functions, and it had the best performances compared with 
      backpropagation (BP) neuro network model and multiple linear regression model 
      (MLRM) both in wintertime and summertime, the smallest value of mean square error 
      (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 
      0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the 
      largest coefficients of determination (R(2)) are 0.6351 and 0.6483, repectively. 
      The ANFIS prediction model could be served as a beneficial strategy for the 
      environment control system that has input parameters with highly fluctuating, 
      complexity, and non-linear relationship.
CI  - Copyright (c) 2016 Elsevier B.V. All rights reserved.
FAU - Xie, Qiuju
AU  - Xie Q
AD  - Institute of Information Technology, Heilongjiang Bayi Agricultural University, 
      Daqing 163319, China. Electronic address: xqj197610@163.com.
FAU - Ni, Ji-Qin
AU  - Ni JQ
AD  - Department of Agricultural and Biological Engineering, Purdue University, West 
      Lafayette, IN 47907, USA.
FAU - Su, Zhongbin
AU  - Su Z
AD  - Institute of Electric and Information, Northeast Agricultural University, Harbin 
      150030, China.
LA  - eng
PT  - Journal Article
DEP - 20161206
PL  - Netherlands
TA  - J Hazard Mater
JT  - Journal of hazardous materials
JID - 9422688
RN  - 0 (Air Pollutants)
RN  - 0 (Gases)
RN  - 7664-41-7 (Ammonia)
SB  - IM
MH  - Air Pollutants/analysis
MH  - Algorithms
MH  - Ammonia/*analysis
MH  - Animal Husbandry/methods
MH  - Animals
MH  - Artificial Intelligence
MH  - *Facility Design and Construction
MH  - *Fuzzy Logic
MH  - Gases
MH  - Humidity
MH  - Linear Models
MH  - *Neural Networks, Computer
MH  - Regression Analysis
MH  - Swine
MH  - Temperature
MH  - Thinking
MH  - Ventilation
OTO - NOTNLM
OT  - Adaptive neuro fuzzy inference system
OT  - Air quality
OT  - Ammonia concentration
OT  - Emissions
OT  - Prediction
EDAT- 2016/12/13 06:00
MHDA- 2018/10/06 06:00
CRDT- 2016/12/13 06:00
PHST- 2016/08/09 00:00 [received]
PHST- 2016/11/17 00:00 [revised]
PHST- 2016/12/05 00:00 [accepted]
PHST- 2016/12/13 06:00 [pubmed]
PHST- 2018/10/06 06:00 [medline]
PHST- 2016/12/13 06:00 [entrez]
AID - S0304-3894(16)31139-6 [pii]
AID - 10.1016/j.jhazmat.2016.12.010 [doi]
PST - ppublish
SO  - J Hazard Mater. 2017 Mar 5;325:301-309. doi: 10.1016/j.jhazmat.2016.12.010. Epub 
      2016 Dec 6.