Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression

Araromi, Dauda Olurotimi; Majekodunmi, Olukayode Titus; Adeniran, Jamiu Adetayo; Salawudeen, T. O.

doi:10.1007/s10661-018-6878-x

Cited by 19 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We then conducted a generalized linear model (GLM) regression to quantify the relative contribution of each variable to desertification. GLM regression extends linear model regressions by expanding the distribution range of dependent variables and introducing a continuous function and is generally applicable to non-normal data (Araromi, Majekodunmi, Adeniran, & Salawudeen, 2018). As Formulae ( 4)-( 6) show, the model is a function of mean μ with a linear combination x β formed from repressor x and coefficient vector β.…”

Section: Generalized Linear Modelmentioning

confidence: 99%

Divergent processes and trends of desertification in Inner Mongolia and Mongolia

et al. 2021

View full text Add to dashboard Cite

Desertification and the expansion of deserts are regarded as a major threat to human livelihoods and ecosystem services, as well as achieving Sustainable Development Goals. Humans have been recognized as significant agents in the expansion of desert-like conditions, through the agency of desertification/land degradation, and through contributions to global climate change. Yet, how desertification changes and whether human or climate change drives these processes and trends over the whole Mongolia Plateau is still not clear. Here we first use MODIS data for 2000-2019 to assess the long-term vegetation changes across the Mongolian Plateau, both in Inner Mongolia of PR China and the Mongolian People's Republic. We then appied a General Linear Model to analyze the additional climate and human attribute data sets to explore relationships between the observed desert extent reduction and their potential drivers. Results show that from 2000-2019, desert extent declined by over 7%, comprised of an almost 11% reduction in Inner Mongolia and 5% reduction in Mongolia, and a reduction in the degree of desert conditions, as measured by vegetation cover grades, across the whole region (37.5% decline -one grade in PR China, 28.75% in Mongolia). The majority of desert area reduction occurs in the northern and eastern areas of the Mongolia Plateau. We found that the main drivers of changing desert extent is not because of a reduction in human pressures on the land, but an increasing trend of both precipitation and temperature, which explains over 75% of all the observed changes. Besides, urbanization, and a reduction of population pressures on the land, contribute to most of the remaining changes. Our results suggest that although the climate has been the primary driver of changing desert area and severity, human interventions have played an important role, although the way it intervenes and the intensity varies in Inner Mongolia and Mongolia. Human's active response to land degradation in the desert contributes to more land restoration in Inner Mongolia than in Mongolia.

show abstract

Section: Generalized Linear Modelmentioning

confidence: 99%

Divergent processes and trends of desertification in Inner Mongolia and Mongolia

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Adapun nilai estimated marginal means pada perlakuan PA10, PA20, P30, dan K secara berurutan yaitu 72,39; 74,38; 71,9, dan 55,82. Estimated marginal means menggambarkan akumulasi scoring pada masing-masing perlakuan berdasarkan parameter yang dimasukkan (Arorami et al, 2018;Damanik-Ambarita et al, 2016). Oleh sebab itu perlakuan PA30 digunakan sebagai rekomendasi pengolahan air limbah pengolahan berdasarkan panjang akar eceng gondok.…”

Section: Gambar 5 Nilai Penurunan Amonia Dan Fosfatunclassified

Kajian Perbaikan Kualitas Air Limbah Pengolahan Kopi Menggunakan Metode Fitoremediasi Dengan Tanaman Eceng Gondok (Eichhornia Crassipes)

Novita

Pradana

2022

j. sains. teknologi.

View full text Add to dashboard Cite

Air limbah pengolahan kopi berpotensi mencemari lingkungan. Eceng gondok merupakan biofilter yang dapat mereduksi polutan pada air limbah. Tujuan penelitian adalah menganalisis variasi panjang eceng gondok terhadap kemampuan reduksi polutan pada air limbah pengolahan kopi menggunakan metode fitoremediasi dengan penambahan aerasi. Terdapat 4 perlakuan fitoremediasi pada pengolahan air limbah menggunakan tanaman eceng gondok yaitu panjang akar 30 cm tanpa aerasi dan panjang akar (10, 20, dan 30 cm) dengan aerasi. Evaluasi reduksi polutan pada air limbah pengolahan kopi diidentifikasi menggunakan analisis efisiensi. Hasil penelitian menunjukkan bahwa fitoremediasi menggunakan eceng gondok dengan aerasi (panjang akar 30 cm) memiliki nilai efisiensi lebih tinggi dari pada tanpa aerasi pada pengolahan air limbah pengolahan kopi. Persentase nilai efisiensi penurunan polutan air limbah pengolahan kopi pada perlakukan panjang akar eceng gondok 30 cm dengan penambahan aerasi yang diindikasikan oleh parameter kekeruhan, BOD, COD, NH3-N, dan PO4-P yaitu sebesar 89,19%; 75,80%; 75,69%; 76,73%; dan 64,99%.

show abstract

“…Of these are feedforward Artificial Neural Networks (ANNs); radial basis functions (RBFs); recurrent neural networks (RNNs); multilayer perceptron (MLP) using backpropagation learning; hybrid models such as adaptive neuro-fuzzy inference (ANFIS). More recently, deep neural networks (DNNs) contain multiple hidden layers and require significant computational power [20,21]. The traditional ANN has a few limitations such as poor generalization due to incorrectly chosen network structure, hard-to-interpret system information stored in neuron weights, and a large amount of data required for accuracy.…”

Section: Artificial Intelligence Used In Modelling Of Wwtpmentioning

confidence: 99%

Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

Deepak

Rustum

2022

Processes

View full text Add to dashboard Cite

The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.

show abstract

Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression

Cited by 19 publications

References 34 publications

Divergent processes and trends of desertification in Inner Mongolia and Mongolia

Divergent processes and trends of desertification in Inner Mongolia and Mongolia

Kajian Perbaikan Kualitas Air Limbah Pengolahan Kopi Menggunakan Metode Fitoremediasi Dengan Tanaman Eceng Gondok (Eichhornia Crassipes)

Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

Contact Info

Product

Resources

About