Study on dye wastewater treatment of tunable conductivity solid-waste-based composite cementitious material catalyst

Kang, Le; Du, Huiling; Xian, Du; HaiTing, Wang; Ma, Wanli; Wang, Mingliang; Zhang, Fengbo

doi:10.5004/dwt.2018.22910

Cited by 80 publications

(27 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advocating for the purchase of ecological products is in line with the current policy of building an ecological civilization in China (Liu and Wang, 2017), (Liu and Liu, 2010), (Kang et al, 2018). In this regard, this study takes eco-product purchase decision-making behavior as a dependent variable to assess the influence of the three dimensions of ecological awareness on the two dimensions of eco-product purchase decision-making behavior.…”

Section: Eco-product Purchase Decision-making Behaviormentioning

confidence: 98%

Consumers’ Eco-product Purchase Decision-Making Behavior from the Perspective of Ecological Awareness

2020

Global NEST: The International Journal

View full text Add to dashboard Cite

<p>In this study, an ecological awareness rating scale and an eco-product purchase decision-making rating scale were developed based on literature and interviews with experts. The ecological awareness variables were subdivided into three aspects: ecological knowledge, ecological emotion, and ecological perception. The eco-product purchase decision-making behavior was also divided into eco-product information gathering behavior and eco-product scheme evaluation behavior. According to consumer decision-making theories, an ecological awareness-ecological product purchase decision-making behavior model was constructed to verify the relationships between variables. Based on regression analysis, the effects of ecological knowledge, ecological emotion, and ecological perception on the two dimensions of consumers’ eco-product purchase decision-making behavior were discussed. It is concluded that ecological awareness has a significant positive impact on the eco-product purchase decision-making behavior, which provides a reference for the development of ecological society and the cultivation of consumers’ ecological consumption habits and methods.</p>

show abstract

Section: Eco-product Purchase Decision-making Behaviormentioning

confidence: 98%

Consumers’ Eco-product Purchase Decision-Making Behavior from the Perspective of Ecological Awareness

2020

Global NEST: The International Journal

View full text Add to dashboard Cite

show abstract

“…The suitable speed of the electric translation platform is 2.3 mm•s -1 . Then the parameters of the imaging acquisition system are set up, the best exposure time and frame frequency are 100 ms and 9 FTPS, respectively (Kang et al, 2018), (Butt et al, 2020). Aronia melanocarpa leaves tile in black cloth, and the reference plate is placed in the field of reference spectrometer.…”

Section: Imaging Spectrum Acquisition and Reflectance Conversionmentioning

confidence: 99%

Hyper Spectral Image Characteristics of Aronia Melanocarpa Leaves under Saline Alkali Stress

2020

Global NEST: The International Journal

View full text Add to dashboard Cite

<p>Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe > moderate > slight > normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.</p>

show abstract

“…[17][18][19] Besides, the rapid growth of economy also brings about a sharp increase in volume of wastewater production, further requiring innovative schemes to generate precise control strategy for WTP through technical means of high performance computation. [20][21][22] Fortunately, longing and yearning of people to intelligence breeds some newly emerging technologies such as articial intelligence, which profoundly facilitates the solution of crossdomain issues. 23,24 As a primary branch of articial intelligence, data mining technology manages to discover hidden information and predict future tendencies from a large amount of historical data by means of statistical learning methods, and has been applied to many industrial scenarios to solve various engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo

Wang

et al. 2020

RSC Adv.

View full text Add to dashboard Cite

show abstract

Study on dye wastewater treatment of tunable conductivity solid-waste-based composite cementitious material catalyst

Cited by 80 publications

References 0 publications

Consumers’ Eco-product Purchase Decision-Making Behavior from the Perspective of Ecological Awareness

Consumers’ Eco-product Purchase Decision-Making Behavior from the Perspective of Ecological Awareness

Hyper Spectral Image Characteristics of Aronia Melanocarpa Leaves under Saline Alkali Stress

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Contact Info

Product

Resources

About