Search results for: Farzad Khajavi

Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

Abstract:

Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Asfaw Gezae Daful, Mohsen Alimandagari, Kathleen Haigh, Somayeh Farzad, Eugene Van Rensburg, Johann F. Görgens

Abstract:

The sugar industry has to 're-invent' itself to ensure long-term economic survival and opportunities for job creation and enhanced community-level impacts, given increasing pressure from fluctuating and low global sugar prices, increasing energy prices and sustainability demands. We propose biorefineries for re-vitalisation of the sugar industry using low value lignocellulosic biomass (sugarcane bagasse, leaves, and tops) annexed to existing sugar mills, producing a spectrum of high value platform chemicals along with biofuel, bioenergy, and electricity. Opportunity is presented for greener products, to mitigate climate change and overcome economic challenges. Xylose from labile hemicellulose remains largely underutilized and the conversion to value-add products a major challenge. Insight is required on pretreatment and/or extraction to optimize production of cellulosic ethanol together with lactic acid, furfural or biopolymers from sugarcane bagasse, leaves, and tops. Experimental conditions for alkaline and pressurized hot water extraction dilute acid and steam explosion pretreatment of sugarcane bagasse and harvest residues were investigated to serve as a basis for developing various process scenarios under a sugarcane biorefinery scheme. Dilute acid and steam explosion pretreatment were optimized for maximum hemicellulose recovery, combined sugar yield and solids digestibility. An optimal range of conditions for alkaline and liquid hot water extraction of hemicellulosic biopolymers, as well as conditions for acceptable enzymatic digestibility of the solid residue, after such extraction was established. Using data from the above, a series of energy efficient biorefinery scenarios are under development and modeled using Aspen Plus® software, to simulate potential factories to better understand the biorefinery processes and estimate the CAPEX and OPEX, environmental impacts, and overall viability. Rigorous and detailed sustainability assessment methodology was formulated to address all pillars of sustainability. This work is ongoing and to date, models have been developed for some of the processes which can ultimately be combined into biorefinery scenarios. This will allow systematic comparison of a series of biorefinery scenarios to assess the potential to reduce negative impacts on and maximize the benefits of social, economic, and environmental factors on a lifecycle basis.

Keywords: biomass, biorefinery, green economy, sustainability

Procedia PDF Downloads 486

Authors: Kathleen Frances Haigh, Mieke Nieder-Heitmann, Somayeh Farzad, Mohsen Ali Mandegari, Johann Ferdinand Gorgens

Abstract:

Lignocellulose can be converted to a range of biochemicals and biofuels. Where this is derived from agricultural waste, issues of competition with food are virtually eliminated. One such source of lignocellulose is the South African sugar industry. Lignocellulose could be accessed by changes to the current farming practices and investments in more efficient boilers. The South African sugar industry is struggling due to falling sugar prices and increasing costs and it is proposed that annexing a biorefinery to a sugar mill will broaden the product range and improve viability. Process simulations of the selected chemicals were generated using Aspen Plus®. It was envisaged that a biorefinery would be annexed to a typical South African sugar mill. Bagasse would be diverted from the existing boilers to the biorefinery and mixed with harvest residues. This biomass would provide the feedstock for the biorefinery and the process energy for the biorefinery and sugar mill. Thus, in all scenarios a portion of the biomass was diverted to a new efficient combined heat and power plant (CHP). The Aspen Plus® simulations provided the mass and energy balance data to carry out an economic assessment of each scenarios. The net present value (NPV), internal rate of return (IRR) and minimum selling price (MSP) was calculated for each scenario. As a starting point scenarios were generated to investigate the production of ethanol, ethanol and lactic acid, ethanol and furfural, butanol, methanol, and Fischer-Tropsch syncrude. The bypass to the CHP plant is a useful indicator of the energy demands of the chemical processes. An iterative approach was used to identify a suitable bypass because increasing this value had the combined effect of increasing the amount of energy available and reducing the capacity of the chemical plant. Bypass values ranged from 30% for syncrude production to 50% for combined ethanol and furfural production. A hurdle rate of 15.7% was selected for the IRR. The butanol, combined ethanol and furfural, or the Fischer-Tropsch syncrude scenarios are unsuitable for investment with IRRs of 4.8%, 7.5% and 11.5% respectively. This provides valuable insights into research opportunities. For example furfural from sugarcane bagasse is an established process although the integration of furfural production with ethanol is less well understood. The IRR for the ethanol scenario was 14.7%, which is below the investment criteria, but given the technological maturity it may still be considered for investment. The scenarios which met the investment criteria were the combined ethanol and lactic acid, and the methanol scenarios with IRRs of 20.5% and 16.7%, respectively. These assessments show that the production of biochemicals from lignocellulose can be commercially viable. In addition, this assessment have provided valuable insights for research to improve the commercial viability of additional chemicals and scenarios. This has led to further assessments of the production of itaconic acid, succinic acid, citric acid, xylitol, polyhydroxybutyrate, polyethylene, glucaric acid and glutamic acid.

Keywords: biorefineries, sugar mill, methanol, ethanol

Procedia PDF Downloads 167