MSEs with the number of hidden nodes.

<p>MSEs with the number of hidden nodes.</p

Adjusted four-step wavelet decomposition forecasting.

<p>Adjusted four-step wavelet decomposition forecasting.</p

Flowchart of multilayered perceptron back-propagation neural networks.

<p>Flowchart of multilayered perceptron back-propagation neural networks.</p

Structure of wavelet decomposition.

<p>Structure of wavelet decomposition.</p

Adjusted two-step wavelet decomposition forecasting.

<p>Adjusted two-step wavelet decomposition forecasting.</p

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

<div><p>Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.</p></div

Henry Hub weekly spot prices from 2000 to 2013.

<p>Henry Hub weekly spot prices from 2000 to 2013.</p

Flow chart of the study.

<p>Flow chart of the study.</p

Nitrogen and phosphorus treatment of marine wastewater by a laboratory-scale sequencing batch reactor with eco-friendly marine high-efficiency sediment

<p>We screened and identified a NH₃–N-removing bacterial strain, <i>Bacillus</i> sp. KGN1, and a removing strain, <i>Vibrio</i> sp. KGP1, from 960 indigenous marine isolates from seawater and marine sediment from Tongyeong, South Korea. We developed eco-friendly high-efficiency marine sludge (eco-HEMS), and inoculated these marine bacterial strains into the marine sediment. A laboratory-scale sequencing batch reactor (SBR) system using the eco-HEMS for marine wastewater from land-based fish farms improved the treatment performance as indicated by 88.2% removal efficiency (RE) of total nitrogen (initial: 5.6 mg/L) and 90.6% RE of total phosphorus (initial: 1.2 mg/L) under the optimal operation conditions (food and microorganism (F/M) ratio, 0.35 g SCOD_Cr/g mixed liquor volatile suspended solids (MLVSS)·d; dissolved oxygen (DO) 1.0 ± 0.2 mg/L; hydraulic retention time (HRT), 6.6 h; solids retention time (SRT), 12 d). The following kinetic parameters were obtained: cell yield (<i>Y</i>), 0.29 g MLVSS/g SCOD_Cr; specific growth rate (<i>µ</i>), 0.06 d⁻¹; specific nitrification rate (SNR), 0.49 mg NH₃–N/g MLVSS·h; specific denitrification rate (SDNR), 0.005 mg /g MLVSS·h; specific phosphorus uptake rate (SPUR), 0.12 mg /g MLVSS·h. The nitrogen- and phosphorus-removing bacterial strains comprised 18.4% of distribution rate in the microbial community of eco-HEMS under the optimal operation conditions. Therefore, eco-HEMS effectively removed nitrogen and phosphorus from highly saline marine wastewater from land-based fish farms with improving SNR, SDNR, and SPUR values in more diverse microbial communities.</p> <p><b>Abbreviations:</b> DO: dissolved oxygen; Eco-HEMS: eco-friendly high efficiency marine sludge; F/M: food and microorganism ratio; HRT: hydraulic retention time; ML(V)SS: mixed liquor (volatile) suspended solids; NCBI: National Center for Biotechnology Information; ND: not determined; qPCR: quantitative real-time polymerase chain reaction; RE: removal efficiency; SBR: sequencing batch reactor; SD: standard deviation; SDNR: specific denitrification rate; SNR: specific nitrification rate; SPUR: specific phosphate uptake rate; SRT: solids retention time; T-N: total nitrogen; T-P: total phosphorus; (V)SS: (volatile) suspended solids; w.w.: wet weight</p