How to analyse and maximise the forest fuel supply availability to power plants in Eastern Finland

The annual use of forest fuels has grown rapidly in Finland during the 21st century. In 2007 the annual use was 5.3 TWh (firewood use excluded), whereas the targeted growth by the year 2010 is 10.6 TWh, i.e. some 5 million m³. The purpose of this work was to evaluate the maximum availability of forest fuels to CHP plants in Eastern Finland. The total availability to the selected CHP plant population was 7 TWh at the maximum transport distance of 100 km. The main share came from logging residues, 3.3 TWh, and the rest from stumps, 1.8 TWh, and small diameter energy wood, 1.9 TWh. The highest plant-specific availability reached the level of 1.7-1.8 TWh, but the overlapping procurement areas reduced the availability for most plants to a level less than 1 TWh. In all plant sites peat fuel could be partially compensated with forest fuels according to availability, but not completely due to the boiler technology. Increasing the targeted national forest fuel use presupposes the use of new logistics supply solutions, such as other transport modes and regional buffer storage networks. This makes it possible to widen the traditional procurement area-based on truck transportation, which is less than 60 km because of a dense plant network.     

Assessment of the availability of agricultural and forest residues for bioenergy production in Romania

This paper provides a resource-based assessment of availability of biomass resources for energy production in Romania, at NUTS-3 level. The estimation of available biomass includes the residues generated from crop production, pruning of vineyards and orchards, forestry operations and wood processing. The estimation of crop residue availability considers several site-specific factors such as crop yields, multi-annual yield variation, environmental constraints and competitive uses. The evaluation of agricultural residues was based on specific residue to product ratios, depending on crop type and crop yield. An estimate of pruning residues is proposed, based on current orchard and vineyard areas and specific ratios of residues. Woody biomass considers forest and forestry residues (including firewood) and wood processing by-products, taking into account the type and share of the unused part of the tree biomass and technical and economic aspects, including availability and competitive use. The amount of agricultural and forest residues available for bioenergy in Romania was estimated at 228.1 PJ on average, of which 137.1 PJ was from annual crop residues, 17.3 PJ residues from permanent crops and 73.7 PJ/year from forestry residues, firewood and wood processing by-products. The biomass availability shows large annual and spatial variations, between 135.6 and 320.0 PJ, due to the variation in crop production and forestry operations. This variation, which is even larger at the NUTS-3 level, if not properly considered may result in shortages in biomass supply in some years, when biomass is available in a lower amount than the average.     

A correlation development for predicting the pressure drop of various refrigerants during condensation and evaporation in horizontal smooth and micro-fin tubes

This paper predicts the condensation and evaporation pressure drops of R32, R125, R410A, R134a, R22, R502, R507a, R32/R134a (25/75 by wt%), R407C and R12 flowing inside various horizontal smooth and micro-fin tubes by means of the numerical techniques of artificial neural networks (ANNs) and non-linear least squares (NLS). In its analyses, this paper used experimental data from the National Institute of Standards and Technology (NIST) and Eckels and Pate, as presented in Choi et al.'s study provided by NIST. In their experimental setups, the horizontal test sections have 1.587, 3.78, 3.81 and 3.97 m long countercurrent flow double tube heat exchangers with refrigerant flowing in the inner smooth (8, 8.01 and 11.1 mm i.d.) and micro-fin (4.339, 5.45, 7.43 and 8.443 mm i.d.) copper tubes and cooling water flowing in the annulus. Their test runs cover a wide range saturation temperatures, vapor qualities and mass fluxes. The pressure drops are calculated with 1485 measured data points, together with analyses of artificial neural networks and non-linear least squares numerically. Inputs of the ANNs of the best correlation are the measured values of the test sections, such as mass flux, tube length, inlet and outlet vapor qualities, critical pressure, latent heat of condensation, mass fraction of liquid and vapor phases, dynamic viscosities of liquid and vapor phases, hydraulic diameter, two-phase density and the outputs of the ANNs, which comprise the experimental total pressure drops of the evaporation and condensation data from independent laboratories. The total pressure drops of in-tube condensation and in-tube evaporation tests are modeled using the artificial neural network (ANN) method of multi-layer perceptron (MLP) with 12-40-1 architecture. Its average error rate is 7.085%, which came from the cross validation tests of 1485 evaporation and condensation data points. Dependency of the output of the ANNs from 12 numbers of input values is also shown in detail, and new ANN based empirical pressure drop correlations are developed separately for the conditions of condensation and evaporation in smooth and micro-fin tubes as a result of the analyses. In addition, a single empirical correlation for the determination of both evaporation and condensation pressure drops in smooth and micro-fin tubes is proposed with an error rate of 14.556%.     

Azeotropic pressure swing distillation of hydrochloric-water for hydrogen production in the CuCl cycle: Thermodynamic and design methods

A pressure swing distillation (PSD) process is designed and analyzed in this paper for the maximum boiling azeotrope separation of an HCl-water binary mixture to recycle concentrated HCl (aq) within the CuCl cycle of thermochemical hydrogen production. Aspen Plus simulation and EES software are used to evaluate the characteristics of the PSD apparatus in terms of flow streams, thermodynamic properties and compositions in the binary azeotropic mixture. A heat transfer and mass transfer analysis (with the McCabe-Thiele method) are also used to predict the height of the packed bed distillation column. Results indicate that both analyses predict the same values for the low and high pressure packing column height of 1.7 m and 2 m, respectively. Due to the component's volatility changes through the azeotropic transition at the HCl-water separation, the minimum and maximum concentration of the HCl (aq) would be at the distillate ports of low and high pressure columns, respectively. Moreover, to break the azeotropic point of HCl (aq) in the PSD system, the minimum required low pressure feed concentration at the operating line slopes of 0.2, 0.4, 0.6 and 0.8 should be 0.086, 0.092, 0.097 and 0.103, respectively. From the results, the re-boiler and condenser heat duties at the high pressure distillation column are more affected with the change in the slope of the operating line, compared to the low pressure side.     

Safety approach for composite pressure vessels for road transport of hydrogen. Part 2: Safety factors and test requirements

Composite pressure vessels for transporting hydrogen on roads are a promising and efficient solution for supplying refueling stations. The safety factors of current ISO design standards are perceived as being restrictive for design. In this paper, new safety factors are calculated based on a probabilistic approach and by extending the methods used in the DNV Offshore standard DNV-OS-C501 “Composite Components”. Short-term and long-term conditions are addressed.     

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net present value is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. The fuzzy set theory is employed to account for the uncertainties associated with electrical power price. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme.     

A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain,Saudi Arabia,Syria, and UAE

This paper presents an adaptive network-based fuzzy inference system-stochastic frontier analysis (ANFIS-SFA) approach for long-term natural gas (NG) consumption prediction and analysis of the behavior of NG consumption. The proposed models consist of input variables of Gross Domestic Product (GDP) and population (POP). Six distinct models based on different inputs are defined. All of trained ANFIS are then compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally the outputs are post-processed (returned to its original scale). To show the applicability and superiority of the integrated ANFIS-SFA approach, gas consumption in four Middle Eastern countries i.e. Bahrain, Saudi Arabia, Syria, and United Arab Emirates is forecasted and analyzed based on the data of the time period 1980–2007. With the aid of autoregressive model, GDP and population are projected for the period 2008–2015. These projected data are used as the input of ANFIS model to predict the gas consumption in the selected countries for 2008–2015. SFA is then used to examine the behavior of gas consumption in the past and also to make insights for the forthcoming years. The ANFIS-SFA approach is capable of dealing with complexity, uncertainty, and randomness as well as several other unique features discussed in this paper.     

On-line implementation of model free controller for oxygen stoichiometry and pressure difference control of polymer electrolyte fuel cell

This paper proposes and validates a model free controller to improve the real time operating conditions of Proton Exchange Membrane Fuel Cells (PEMFC). This approach is based on an ultra-local model that does not depend on a precise knowledge of the system. It is perfectly adapted to a complex system such as the fuel cell, while benefiting from the ease of online implementation and low computational cost. The designed controller is used to regulate both the oxygen stoichiometry and the membrane inlet pressure, which are crucial operating conditions for the fuel cell's lifetime. The objectives of the proposed control strategy are twofold: preventing the starvation failure, and limiting the potential for mechanical degradation of the membrane during a large pressure difference. The performance of the proposed control strategy is initially evaluated by a simulation environment for both oxygen stoichiometry and inlet pressure difference control of fuel cell stack. An online validation on 1.2 KW fuel cell stack is conducted to control the membrane pressure drop. Two case studies are comprehensively investigated in relation to stoichiometry control: set point tracking and rejection of unmeasured disturbances caused by current variations. Simulations and experimental results reveal that the proposed controller provides significantly better performance in terms of fast trajectory tracking, and ensures less overshoot compared to the Fuzzy PID and PID controller. This efficiency is proven using the Integral Absolute Error (IAE), Integral Squared Error (ISE) and Integral of the Square input (ISU) performance indexes.     

A new optimization approach to energy network modeling: anthropogenic CO2 capture coupled with enhanced oil recovery

To meet next generation energy needs such as wind‐ and solar‐generated electricity, enhanced oil recovery (EOR), CO₂ capture and storage (CCS), and biofuels, the US will have to construct tens to hundreds of thousands of kilometers of new transmission lines and pipelines. Energy network models are central to optimizing these energy resources, including how best to produce, transport, and deliver energy‐related products such as oil, natural gas, electricity, and CO₂. Consequently, understanding how to model new transmission lines and pipelines is central to this process. However, current energy models use simplifying assumptions for deploying pipelines and transmission lines, leading to the design of more costly and inefficient energy networks. In this paper, we introduce a two‐stage optimization approach for modeling CCS infrastructure. We show how CO₂ pipelines with discrete capacities can be ‘linearized’ without loss of information and accuracy, therefore allowing necessarily complex energy models to be solved. We demonstrate the new approach by designing a CCS network that collects large volumes of anthropogenic CO₂ (up to 45 million tonnes of CO₂ per year) from ethylene production facilities and delivers the CO₂ to depleted oil fields to stimulate recovery through EOR. Utilization of anthropogenic CO₂ has great potential to jumpstart commercial‐scale CCS while simultaneously reducing the carbon footprint of domestic oil production. Model outputs illustrate the engineering challenge and spatial extent of CCS infrastructure, as well as the costs (or profits) of deploying CCS technology. We show that the new linearized approach is able to offer insights that other network approaches cannot reveal and how the approach can change how we develop future energy systems including transporting massive volumes of shale gas and biofuels as well as electricity transmission for wind and solar energy. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator (DFIG) wind turbines. At present, to the best of the authors’ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabilistic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table (COPT), considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.     

A comprehensive power loss,efficiency, reliability and cost calculation of a 1 MW/500 kWh battery based energy storage system for frequency regulation application

Battery based energy storage system (ESS) has tremendous diversity of application with an intense focus on frequency regulation market. An ESS typically comprised of a battery and a power conversion system. A calculation of performance parameters is performed in this research. The aim is to formulate an in-depth analysis of the ESS in terms of power losses of the semiconductor and electrical devices, efficiency, reliability and cost which would foster various research groups and industries around the globe to improve their future product. In view of this, a relation between the operating conditions and power losses is established to evaluate the efficiency of the system. The power loss calculation presented in this paper has taken into account the conduction and switching losses of the semiconductor devices. Afterwards, the Arrhenius Life Stress relation is adopted to calculate the reliability of the system by considering temperature as a covariate. And finally, a cost calculation is executed and presented as a percentage of total cost of the ESS. It has been found that the power loss and efficiency of the ESS at rated power is 146 kW and 85% respectively. Furthermore, the mean time between failures of the ESS is 8 years and reliability remains at 73% after a year. The major cost impact observed is for battery and PCS as 58% and 16% respectively. Finally, it has been determined that further research is necessary for higher efficient and lower cost system for high penetration of energy storage system in the market.     

A new approach in MPPT for photovoltaic array based on Extremum Seeking Control under uniform and non-uniform irradiances

This work presents a Maximum Power Point Tracking (MPPT) based on analyzing the output characteristics of PV array under uniform irradiance and partial shading conditions. In order to carry out MPPT in PV panels, under partial shading conditions a method based on Extremum Seeking Control (ESC) is introduced. In contrast with classic ESC, in this method the double of dithering signal frequency is not used, consequently PV output power has a ripple of a lower frequency. Also the drop which occurs when MPPT system starts to operate in classic ESC method is minimized in this paper. The ESC approach for MPPT in this paper uses a series combination of a Low Pass Filter (LPF) and a High Pass Filter (HPF). These two filters act as a Band Pass Filter (BPF) and let a specific frequency of input power which includes the derivative of PV with respect to its voltage pass through. Finally, the system does not operate in local optimal points for efficient point will be global. The algorithm adds partial shadow judging conditions in ESC method. The system runs the variable step ESC method to realize MPPT when photovoltaic array is under uniform irradiance. Under Partial Shading Condition (PSC), the control method can eliminate the interference of local maximum power point (MPP) to make 23 the PV array running at global MPP. In addition, unlike other methods, the proposed MPPT operates on the global MPPs. The proposed MPP tracker does not add any extra complexity compared to the classical ones. However, it increases significantly the efficiency of the PV installation under PSC. We will show that under uniform irradiance, the proposed MPPT leads to faster performances than classical approaches.     

A simple and effective approach for evaluating unconfined hydrogen/air cloud explosions

The topic of hydrogen safety assessment has been focused by many researchers. The overpressure evaluation of vapor cloud explosion (VCE), is an important issue for both designing and evaluating on chemical plants, as well as buildings. Unknown flame radius history limits the original acoustic approximation model's application. The objective of this work is to develop an achievable model for hydrogen/air deflagration assessment in engineering applications, and the model should have high computational efficiency. A tentative scheme that starts from flame/piston speed history solving was adopted, and the flame/piston radius and acceleration history will be obtained subsequently. Thus, the overpressure history for far field could be gotten based on the acoustic approximation model. A simplified scheme was employed for the region inside the flame cloud. The model proposed in this paper could be solved in several seconds, because there are no differential equations but only algebraic equations. The model was verified by hydrogen/air deflagration tests from small scale to large scale. Compared with the experimental data, the model appeared well agreements in the medium and large scale cases. In the small scale cases, the model obtained acceptable solutions.     

Selection of sustainable prime mover for combined cooling,heat, and power technologies under uncertainties: An interval multicriteria decision‐making approach

This study aims at developing an interval multicriteria decision‐making method for helping the stakeholders to select the most sustainable prime mover for combined cooling, heat, and power (CCHP) technologies under uncertainties for promoting the sustainable development of CCHP system. The “interval best‐worst method,” which can address the vagueness and ambiguity existing in the judgments of the decision‐makers, has been developed for determining the weights of the evaluation criteria. The interval VIKOR method which can rank the alternatives with imprecise data has been used to prioritize the alternative prime movers for CCHP technologies. Four alternative prime movers including internal combustion engine, gas turbines, microturbines, and fuel cells were studied by the developed method, and the sustainability order of the 4 prime movers from the most sustainable to the least is fuel cells, microturbines, gas turbines, and internal combustion engine. Sensitivity analysis was also carried out to investigate the influences of the weights of the sustainability criteria on the sustainability ranking of the alternative prime movers, and the results reveal that the sustainability rankings are very sensitive to the weights of sustainability criteria.     

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.     

A new and facile approach for the preparation of cross-linked sulfonated poly(sulfide sulfone) membranes for fuel cell application

A new and facile approach has been developed for the preparation of cross-linked sulfonated poly(sulfide sulfone) (SPSSF) membranes. The cross-linking reaction was performed by immersing the SPSSF membranes into polyphosphoric acid at 180 °C for 1.5 h and the cross-linking bond was the very stable sulfonyl group. Cross-linking significantly improved the membrane performance, i.e., the cross-linked membranes showed better mechanical properties, lower water uptake and lower methanol permeability than the corresponding uncross-linked ones, while reasonably high proton conductivity was maintained. For example, for the membrane containing 40 mol% sulfonated moiety, by cross-linking the tensile strength increased from 39 MPa (dry) or 21 MPa (wet) to 44 MPa (dry) or 30 MPa (wet) and the elongation at break from 17% (dry) or 18% (wet) to 65% (dry) or 21% (wet), while the water uptake was reduced from 74 to 38 wt% and the methanol permeability from 7.0 × 10⁻⁷ to 1.6 × 10⁻⁷ cm² s⁻¹ (30 °C). The proton conductivity, however, did not decrease too much (from 0.076 to 0.043 S cm⁻¹ in water at 30 °C).     

Prospects for and barriers to domestic micro-generation: A United Kingdom perspective

Approximately 38% of current UK greenhouse gas emissions can be attributed to the energy supply sector. Losses in the current electricity supply system amount to around 65% of the primary energy input, mainly due to heat wasted during centralised production. Micro-generation and other decentralised technologies have the potential to dramatically reduce these losses because, when fossil fuels are used, the heat generated by localised electricity production can be captured and utilised for space and water heating. Heat and electricity can also be produced locally by renewable sources. Prospects and barriers to domestic micro-generation in the UK are outlined, with reference to the process of technological innovation, energy policy options, and the current status of the micro-generation industry. Requirements for the main technology options, typical energy outputs, costs to consumers, and numbers of installed systems are given where data is available. It is concluded that while micro-generation has the potential to contribute favourably to energy supply, there remain substantial barriers to a significant rise in the use of micro-generation in the UK.     

A novel synthesis approach to partially fluorinated sulfonimide based poly (arylene ether sulfone) s for proton exchange membrane

Innovation of novel low cost proton conductive materials with super acidity has been the ever-increasing thirst for PEMFCs. Sulfonimide groups have the strongest gas-phase super-acidity with excellent thermal and chemical stability. Therefore, a series of partially fluorinated sulfonimide functionalized poly(arylene ether sulfone)s (SIPAES-xx) were successfully synthesized by chemical modification of sulfonated polyarylethersulfone (SPAES). The SPAESs were synthesized first by the direct polymerization of 4,4′ -dichlorodiphenylsulfone (DCDPS), 3,3′-disulfonate-4,4′-dichlorodiphenylsulfone (SDCDPS), and bisphenol. Thereafter, all arylsulfonic acid groups were converted into more acidic sulfonimide acid groups using partial fluorinated fluorosulfonyl imide monomer. The effect of the conversion of arylsulfonic acid function into sulfonimide was evaluated through thermal and chemical analysis. ¹H-NMR, FTIR, TGA, FE SEM, and AFM were used to illustrate the structure, thermal and chemical properties of (SIPAES-xx) membranes. The membranes showed IEC values of 0.78–1.41 mequiv./g with 6.7–40.6% water uptake for 20–40% ionic groups. The synthesized SIPAES-40 membranes showed comparable proton conductivity to Nafion^® 117 at same conditions. Nevertheless, the aromatic sulfonimide remained stable up to 370 °C. Furthermore, the presence of fluorine within the sulfonimide polymer provided high dimensional stability and oxidative durability by protecting the polymer chain from oxidizing radical species. Therefore, the synthesized SIPAES-xx membranes have the potential features as a proton exchange membrane (PEM) materials in the fuel cell.     

A systematic approach for design and simulation of monoethylene glycol (MEG) recovery in oil and gas industry

Monoethylene glycol (MEG) is a widely used hydrate inhibitor in the oil and gas industry to reduce the risk of hydrate formation in pipelines that could cause a blockage. For flow assurance and hydrate inhibition purposes, large volumes of MEG are required to control the hydrate formation conditions in pipelines. Disposal of rich MEG after separation has considerable costs and poses significant environmental concerns. Therefore, the development of an effective process for MEG recovery has been gaining importance. The current study presents a systematic approach to develop, simulate, and optimize MEG recovery using Aspen Plus and ELEC-NRTL thermodynamic package. Two distinct MEG recovery process (MEG-R-P) designs, namely, the vacuum (design I) and atmospheric (design II) distillation, were tested. Both designs have demonstrated exceptional performance in recovering MEG from salts and water and producing lean MEG at a purity of 90 wt%. Design I operating at vacuum conditions outweighs design II in terms of MEG purity and energy requirements. The addition of MEG-R-P has the advantage of recovering and reusing significant amounts of MEG and removing the burden on the environment.     

Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy

Data envelopment analysis (DEA) has been widely used in energy efficiency and environment efficiency analysis in recent years. Based on the existing environment DEA technology, this paper presents several DEA models for estimating the aggregated efficiency of resource and environment. These models can evaluate DMUs’ energy efficiencies and environment efficiencies simultaneously. However, efficiency ranking results obtained from these models are not the same, and each model can provide some valuable information of DMUs’ efficiencies, which we could not ignore. Under this situation, it may be hard for us to choose a specific model in practice. To address this kind of performance evaluation problem, the current paper extends Shannon-DEA procedure to establish a comprehensive efficiency measure for appraising DMUs’ resource and environment efficiencies. In the proposed approach, the measure for evaluating a model's importance degree is provided, and the targets setting approach of inputs/outputs for DMU managers to improve DMUs’ energy and environmental efficiencies is also discussed. We illustrate the proposed approach using real data set of 30 provinces in China.