• Energy Research

A refining model is developed to analyses the refining process’s energy efficiency based on the refining variables. A simulation model is obtained for longer-term refining energy analysis by further developing the MATLAB Thermo-Mechanical Pulping Simulink toolbox. This model is utilized to predict two essential variables for refining energy efficiency calculation: refining motor-load and generated steam. The conventional variable for presenting refining energy efficiency is refining specific energy consumption (RSEC), which is the ratio of the refining motor load to throughput and does not consider the share of recovered energy from the refining produced steam. In this study, a new variable, corrected refining specific energy consumption (CRSEC), is introduced and practiced for better representation of the refining energy efficiency. In the calculation process of the CRSEC, recovered energy from the refining generated steam is considered useful energy. The developed model results in 160% and 78.75% improvement in simulation model determination coefficient and error, respectively. Utilizing the developed model and hourly district heating demand for CRSEC calculation, results prove a 22% annual average difference between CRSEC and RSEC. Findings confirm that the wintertime refining energy efficiency is 27% higher due to higher recovered energy in the heat recovery unit compared to summertime.

The energy coming from solar radiation could be harvested and trans-formed into electricity through the use of solar-thermal power generation and photovoltaic (PV) power generation. Placement of solar collectors (thermal and photovoltaic) affects the amount of incoming radiation and the absorption rate. In this research, new correlations for finding the monthly optimum slope angle (OSA) on flat-plate collectors are proposed. Twelve equations are developed to calculate the monthly OSA by the linear regression model, for the northern and the southern hemisphere stations from 15° to 55° and –20° to –45°, respectively. Also, a new equation for calculating the yearly tilt angle is developed and compared with several other calculation methods from the literature. Results confirm a 20% increase in solar energy absorption by adjusting the collectors’ tilt angle in monthly time periods. This is while the adjusted collectors with the yearly optimum slope angle receive approximately 7% higher solar radiation compared to the horizontal collectors. Furthermore, the proposed equations outperformed the other calculation methods in the literature.

Publisher Copyright: © 2021, Tech Science Press. All rights reserved. Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs). For each of the derived IMFs, a different LSTM model is trained. Finally, the outputs of all the individual LSTM learners are fed to a meta-learner to provide an aggregated output for the energy demand prediction. The suggested methodology is applied to the California ISO dataset to demonstrate its applicability. Additionally, we compare the output of the proposed algorithm to a single LSTM and two state-of-the-art data-driven models, specifically XGBoost, and logistic regression (LR). The proposed hybrid model outperforms single LSTM, LR, and XGBoost by, 35.19%, 54%, and 49.25% for short-term, and 36.3%, 34.04%, 32% for long-term prediction in mean absolute percentage error, respectively. Peer reviewed

In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimization algorithms are particle swarm optimization algorithm (PSO), differential evolution (DE), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimization algorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionary algorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process.

Abstract The efficient and correct design of an Energy Hub (EH) is associated with the improvement in energy conversion efficiency and EH profitability. The novel method of coupling thermo-economic analysis with reliability and risk assessment offers incredible potential in improving the overall performance of the whole system from the cost and energy-saving aspect. In this study, a cost-efficient EH plant consisting of combined cooling, heating, and power (CCHP) system is designed to be economically optimum regarding the EH operator. On the demand side, the energy consumer is a high-rise residential building that provides its cooling and heating demands through the EH. As a new optimization approach, an optimum cost-efficient EH has been designed by coupling the thermo-economic analysis along with reliability and availability assessments. System total cost is compared with the conventional planning method, where the system availability and reliability of the EH components are not considered in the optimization model. The new planning method reveals 119%, 69%, 74%, and 16% reduction in the system energy cost, demand penalty cost, operation cost, and total cost during the EH life span, respectively. Additionally, a new index as “real availability” is calculated and introduced based on the energy demand profile of the EH. Unlike the Markov method, where an available system is defined in such a way that all subsystems are healthy, the new approach introduces the EH availability following the energy demand profile. In this regard, results prove a vast difference comparing Markov-based availability and system real availability.