scholarly journals Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

2021 ◽  
Vol 9 (2) ◽  
pp. 324
Author(s):  
Celina Dittmer ◽  
Johannes Krümpel ◽  
Andreas Lemmer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Biogas Production ◽  
Solid Substrate ◽  
Full Scale ◽  
Percentage Error ◽  
Measurement Technology ◽  
Series Analysis ◽  
Substrate Supply ◽  
Extensive Evaluation

Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14–18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate’s composition.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

scholarly journals Economic feasibility of biogas production in swine farms using time series analysis

2016 ◽  
Vol 46 (7) ◽  
pp. 1295-1300 ◽  
Author(s):  
Felipe Luis Rockenbach ◽  
Adriano Mendonça Souza ◽  
João Helvio Righi de Oliveira
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Biogas Production ◽  
Electric Energy ◽  
Economic Feasibility ◽  
Production Costs ◽  
Financial Investment ◽  
Swine Farms ◽  
Series Analysis

ABSTRACT: This study aimed to measure the economic feasibility and the time needed to return capital invested for the installation of a swine manure treatment system, these values originated the sale of carbon credits and/or of compensation of electric energy in swine farms, using the Box-Jenkins forecast models. It was found that the use of biogas is a viable option in a large scale with machines that operate daily for 10h or more, being the return period between 70 to 80 months. Time series analysis models are important to anticipate the series under study behavior, providing the swine breeder/investor means to reduce the financial investment risk as well as helping to decrease the production costs. Moreover, this process can be seen as another source of income and enable the breeder to be self-sufficient in the continuous supply of electric energy, which is very valuable nowadays considering that breeders are now increasingly using various technologies.

Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network

2019 ◽  
Vol 80 (2) ◽  
pp. 243-253 ◽  
Author(s):  
Qianqian Zhang ◽  
Zhong Li ◽  
Spencer Snowling ◽  
Ahmad Siam ◽  
Wael El-Dakhakhni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Time Series Analysis ◽  
Percentage Error ◽  
Flow Data ◽  
Series Analysis ◽  
Artificial Neural

Abstract Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties, such as the nonlinear precipitation-runoff relationships in combined sewer systems, unpredictability due to aging infrastructure, and frequently inconsistent data quality. To address such challenges, a time series analysis model (i.e., the autoregressive integrated moving average, ARIMA) and an artificial neural network model (i.e., the multilayer perceptron neural network, MLPNN) were developed for predicting wastewater inflow. A case study of the Barrie Wastewater Treatment Facility in Barrie, Canada, was carried out to demonstrate the performance of the proposed models. Fifteen-minute flow data over a period of 1 year were collected, and the resampled daily flow data were used to train and validate the developed models. The model performances were examined using root mean square error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency. The results indicate that both models provided reliable forecasts, while ARIMA showed a slightly better performance than MLPNN in this case study. The proposed models can provide useful decision support for the optimization and management of WWTPs.

Forecasting of Production and Consumption of Ethanol in Thailand Using Time Series Analysis

2015 ◽  
Vol 781 ◽  
pp. 651-654
Author(s):  
Sasiwimon Sriyotha ◽  
Rojanee Homchalee ◽  
Weerapat Sessomboon
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Ethanol Production ◽  
Agricultural Products ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Series Analysis ◽  
Production And Consumption ◽  
The Mean

Ethanol is the important renewable energy in Thailand. It is alcohol produced from sugarcane and tapioca that are agricultural products available in Thailand. Ethanol is used to blend with gasoline for use as gasohol. Ethanol production and consumption in Thailand are fluctuating. Consequently, planning of ethanol production and consumption is irrelevant. In order to solve this problem, this study aims to find forecasting models using time series analysis including exponential smoothing and the Box-Jenkins methods. The most appropriate forecasting model was selected from the two methods by considering the minimum of the mean absolute percentage error: MAPE. It was found that the Box-Jenkins is the most appropriate method to forecast both ethanol production and consumption. The forecasting results were then used to determine appropriate quantity and proportion of molasses and tapioca needed for ethanol production in the future.

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