Browsing by Author "Talebjedi, Behnam"

Now showing 1 - 12 of 12

Advanced design and operation of Energy Hub for forest industry using reliability assessment
(2023-07-25) Talebjedi, Behnam; Laukkanen, Timo; Holmberg, Henrik; Syri, Sanna
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A large part of the refining heat production in the thermomechanical pulp mill can be recovered to supply the paper machine heat demand. This study introduces a novel approach for the heat integration of a thermomechanical pulp mill and paper machine using Energy Hub. An Energy Hub consisting of a steam generator heat pump and the electric boiler is integrated with the thermomechanical pulp mill to provide the heating demand of the paper machine. The advanced cost-efficient design and operation of the Energy Hub are investigated in this research by integrating thermo-economic analysis, reliability & availability assessment, and load profile prediction. The thermo-economic analysis combines economics and thermodynamics, which is necessary for energy system unit commitments. Reliability assessment will lead to more accurate modeling of real-life system operating conditions since system components' availability is considered in the design process. Load profile prediction estimates the Energy Hub load for the next hour, which helps with the optimal operation of the Energy Hub. Different state-of-the-art long-short-term memory (LSTM) neural network models have been developed to achieve the best time series model for refining heat prediction in the thermomechanical pulp mill. Results show that all the time series models are effective for refining heat prediction, while Bidirectional LSTM appears to perform better than others with the correlation coefficient and root mean square error of 0.9 and 0.15, respectively. In addition, the proposed Energy Hub design approach is compared with the conventional design method. The proposed design method offers a robust design that isn't impacted by unsupplied demand penalty rates. Depending on the penalty rates, the total system cost could decrease by 14%-28% utilizing the proposed design method.
Dataset of design factors and corresponding properties for sustainable design of cellulose lightweight materials
(2025-05) Zhu, Yeling; Talebjedi, Behnam; Zhang, Weijia; Tang, Zirui; Jiang, Feng; Tu, Qingshi
Short survey
Efficient energy solution for buildings: Leveraging long-term heat storage for optimal operation and cost savings
(2023-12-11) Tanny, Nusrat
Sähkötekniikan korkeakoulu | Master's thesis
As of 2020, Residential heating accounted for a substantial 39 TWh of energy consumption in Finland, primarily sourced from district heat, wood, and electricity, constituting 82% of heating sources. Despite the vital role of indoor heating in addressing weather challenges and high heating demands, its reliance on fossil fuels poses significant obstacles. Amid the transition towards sustainable energy, Thermal Energy Storage (TES) emerges as pivotal for improving energy efficiency. Recently phase change materials (PCM), particularly cold-crystallizing materials (CCM), have gained attention as a potential long-term heat storage solution. CCM, composed of erythritol within a polymer matrix, exhibits the capability to store heat for the long term without efficiency loss. This material achieves heat storage by cooling to a deeply supercooled state and releases stored energy through cold crystallization, making it a promising candidate for a scalable and efficient energy storage medium. This thesis aims to optimize electricity consumption costs for a year by strategically charging and discharging during low-price periods and discharging during power price variations. This study utilizes a Mixed-Integer Linear Programming (MILP) model considering three storage units based on CCM. The findings showcase potential cost reductions of 2.58%, 4.74%, and 5.89% with one, two, and three storage units, respectively, compared to a non-storage system. Current research highlights the potential possibility of CCM-based storage as a means of reducing power consumption costs and promoting energy efficiency. However, additional research and experimentation are necessary to validate these findings and address the limitations of this approach.
Electricity demand time series forecasting based on empirical mode decomposition and long short-term memory
(2021) Taheri, Saman; Talebjedi, Behnam; Laukkanen, Timo
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Energy Efficiency Analysis of the Refining Unit in Thermo-Mechanical Pulp Mill
(2021-03-17) Talebjedi, Behnam; Laukkanen, Timo; Holmberg, Henrik; Vakkilainen, Esa; Syri, Sanna
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Energy modeling of a refiner in thermo-mechanical pulping process using ANFIS method
(2020-10-01) Talebjedi, Behnam; Khosravi, Ali; Laukkanen, Timo; Holmberg, Henrik; Vakkilainen, Esa; Syri, Sanna
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Integration of thermal energy storage for sustainable energy hubs in the forest industry : A comprehensive analysis of cost, thermodynamic efficiency, and availability
(2024-09-15) Talebjedi, Behnam; Laukkanen, Timo; Holmberg, Henrik
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Thermal energy storage (TES) offers a practical solution for reducing industrial operation costs by load-shifting heat demands within industrial processes. In the integrated Thermomechanical pulping process, TES systems within the Energy Hub can provide heat for the paper machine, aiming to minimize electricity costs during peak hours. This strategic use of TES technology ensures more cost-effective and efficient energy consumption management, leading to overall operational savings. This research presents a novel method for optimizing the design and operation of an Energy Hub with TES in the forest industry. The proposed approach for the optimal design involves a comprehensive analysis of the dynamic efficiency, reliability, and availability of system components. The Energy Hub comprises energy conversion technologies such as an electric boiler and a steam generator heat pump. The study examines how the reliability of the industrial Energy Hub system affects operational costs and analyzes the impact of the maximum capacities of its components on system reliability. The method identifies the optimal design point for maximizing system reliability benefits. To optimize the TES system's charging/discharging schedule, an advanced predictive method using time series prediction models, including LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), has been developed to forecast average daily electricity prices. The results highlight significant benefits from the optimal operation of TES integrated with Energy Hubs, demonstrating a 4.5–6 percent reduction in system operation costs depending on the reference year. Optimizing the Energy Hub design improves system availability, reducing operation costs due to unsupplied demand penalty costs. The system's peak availability can reach 98 %, with a maximum heat pump capacity of 2 MW and an electric boiler capacity of 3.4 MW. The GRU method showed superior accuracy in predicting electricity prices compared to LSTM, indicating its potential as a reliable electricity price predictor within the system.
A Literature Review on Artificial Intelligence Applications in Wind Power Plants
(2020-09-01) Heinonen, Heta
Insinööritieteiden korkeakoulu | Bachelor's thesis
New correlations for determination of optimum slope angle of solar collectors
(2020-01-01) Khosravi, Ali; Rodriguez, Oscar Ricardo Sandoval; Talebjedi, Behnam; Laukkanen, Timo; Pabon, Juan Jose Garcia; Assad, Mamdouh El Haj
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Parametric Models for Forest Industry Transformation in Energy Efficiency: Machine Learning Approach
(2023) Talebjedi, Behnam
School of Engineering | Doctoral dissertation (article-based)
This thesis is based on industrial projects with Pulp and Paper industry in a Nordic country. The main focus of the thesis is on the energy efficiency development of the thermomechanical pulp (TMP) mill and optimal integration of the TMP mill and paper machine through heat recovery and the concept of an Energy Hub. Advanced statistical approaches and machine learning methods have been employed to develop refining identification models and advanced energy-saving refining optimization methods for the TMP process. Results prove that an accurate refining identification model could be developed through advanced machine learning methods. The refining identification models to predict the refining energy (such as specific energy consumption) and final pulp quality (such as freeness and fiber length) can be further used to develop a refining control and optimization strategy. The developed optimization strategy based on the integration of Machine learning methods and Genetic optimization algorithm confirms an average reduction of 14 % for the total refining-specific energy consumption. In the following, the optimal integration of the TMP mill and paper machine has been investigated through the Energy Hub (EH) concept. The proposed approach for the cost and energy-efficient design and operation of EH is based on the integration of thermo-economic analysis, reliability and availability analysis, and EH load prediction. The proposed approach was first introduced and evaluated for the energy and cost-efficient design of a combined cooling, heating, and power (CCHP) system that provides the hourly thermal demand of a high-rise residential building. Results prove that by utilizing the proposed method, the system's average total cost could be reduced by 16% during the system's lifespan. As the presented method has shown to be effective in residential EH applications, this method was examined in a second case study (the forest industry) to determine the optimal integration of TMP mill and paper machines. The proposed design method offers a robust design that isn't impacted by penalty rates of unsupplied demand. Depending on the penalty rates, the total system cost could decrease by 14%-28% utilizing the proposed design method.
Solar-assisted Waste Heat Utilisation Coupled with Thermal Energy Storage for Electricity Production: Technical and Economic Assessment
(2025-02-03) Skorniakov, Ilia
School of Chemical Engineering | Master's thesis
The amount of energy dissipated as ultra-low-temperature waste heat constitutes approximately 45% of global primary energy consumption. This waste heat poses environmental risks by increasing local temperatures and imposing unnecessary costs on industries through elevated fuel consumption and equipment maintenance. Thus, addressing this challenge is essential for mitigating climate change and improving the energy efficiency of industrial sites. This thesis reviewed four pathways for waste heat utilisation: conversion of waste heat into useful heat, cooling, electricity, and process integration. Various technologies were examined, including heat pumps, chillers, Rankine cycle modifications, solar thermal collectors, and thermal energy storage systems. Among these options, electricity generation was selected as the desired application, and an innovative setup was proposed. The proposed system upgrades 85°C waste heat from pulp and paper mill to 120°C using solar thermal collectors and converts it into electricity via an organic Rankine cycle, which is sold to the local grid. To ensure consistent operation, the system additionally incorporates two thermal energy storages: one to store waste heat and another to store upgraded heat. A techno-economic assessment of the proposed system was conducted for industrial sites in Finland, Spain, and Greece, using one year of weather and electricity price data. The system’s lifespan is estimated to be 25 years, and the analysis revealed best-case payback periods of 99, 13, and 11 years, respectively, with annual electricity production ranging from 4.5 to 5.5 GWh. However, less than 50% of the available waste heat was utilised. The system’s profitability was found to rely heavily on daily electricity price fluctuations rather than longer-term trends. The proposed setup presents a sustainable, though less profitable, alternative to photovoltaic panels for electricity production in regions with high solar irradiation and electricity prices. Alternatively, in regions with low solar irradiation, the system could be adapted to upgrade waste heat for use in district heating networks, offering a practical and environmentally friendly application for ultra-low temperature waste heat recovery.
Technoeconomic analysis of storing industrial waste heat in a novel heat storage
(2024-01-22) Javaherneshan, Dorin
Insinööritieteiden korkeakoulu | Master's thesis
In remote areas where direct electric heating is the dominant heating system, it is essential to provide alternative heat supply options. Electricity prices fluctuate considerably, and considerable amounts of electricity are still produced from fossil fuels with a negative impact on the environment. Detached houses in Nordic countries, including Finland, require a lot of heating, but at the same time, a considerable amount of excess heat is available from industrial processes. A significant amount of this excess heat cannot be utilized for energy efficiency enhancement at the process site and is therefore wasted. In rural areas, there is normally no heating network connecting houses to one another, and there is thus no existing infrastructure to enable usage of waste heat from industries for space-heating purposes. Thermal Energy Storage (TES) is an option that can store the unused heat of industry and transfer it to houses. A novel Latent TES has been developed utilizing cold-crystallization material (CCM) that enables long storage periods which can be suitable for satisfying the annual space heating demand of a small house. The storage medium can be charged once or several times at the industry during a year and store the heat for months. This thesis aims to identify and select industries that can efficiently recover and distribute their excess heat using the CCM TES system. Thereafter, various storage models and case studies are proposed, taking into account factors such as the number of charging and transportation cycles, and storage unit quantities. For each model, the design entails storage size (s), heat exchanger (s), potential pipe network, valves, and the pump. Technoeconomic analysis is conducted for each model and case study in order to assess the economic efficiency of the CCM TES for domestic space heating purposes. Net Present Value (NPV) and Levelized Cost of Energy (LCOE) are used to conduct an economic evaluation and make comparisons with alternative heating systems for single-family houses, such as direct electric heating, air source heat pumps, and ground source heat pumps. In this regard, the study aimed to determine the most cost-effective solution. The study's findings demonstrate that increasing the TES's number of charging cycles per year decreases its size and improves the system's economic efficiency beyond the other options explored. The LCOE decreased from 0.59 to 0.15 €/kWh, which is comparable with the average household electricity price of 0.09 €/kWh.