ABSTRACT. Production in FPSOs (Floating, Production, Storage, and Offloading) faces a dual challenge: meeting variation in energy demand while decreasing the environmental effects. Reservoir fluid composition strongly influences the FPSO operation, including the compression units resulting in changes to the total power consumption, desired productions, efficiency, and sustainability. The main goals of this work is to apply a thermodynamic analysis and to identify the contribution of the main effects in an FPSO with CCS (Carbon Chapter and Storage) operating parameters by using two screening analysis methods (SS-ANOVA and PAWN) on the following responses: 1) Total power consumption of FPSO and 2) separation efficiency of CCS. The real crude oil compositions with the variation of GOR (gas-oil ratio) and CO2 content using the empirical performance data of power and heat generation unit are considered in the current research. The results of the thermodynamic analysis showed that the power and heat generation units and CCS represent the principal share of the total irreversibilities for all studied cases (up to 72% of total exergy loss). Moreover, it was highlighted from the sensitivity analysis that the total power consumption is much more sensitive to the operating parameters of the separation train in the case with CO2-lean and moderate GOR than the case with CO2-rich and high GOR.

ABSTRACT. Biomass, as a renewable energy source, has the advantage of being converted into useful energy such as heat, electricity or biofuels. Despite the increasing interest in the forest biomass use, the high logistic costs represent almost 90% of the total bioenergy production cost. Thus, the supply chain cost of forest-based biomass for energy generation is an important factor affecting the selling prices of all wood biofuels and it affects its competition when compared with fossil fuels. Biomass logistics comprises several activities: (1) harvesting and collection; (2) storage; (3) pre-processing; and (4) transportation. The main objective of this work is to analyse the complete supply chain of different forest-based biomass production and estimate the full cost structure and the biomass respective energy value. The transportation cost depends on distances, transport option and load volume. Biomass can be collected in different forms and the harvesting costs of forest biomass vary mainly due to different working methods and distinct machinery used for cutting or splitting the biomass. Biomass can be stored at intermediate storage facilities or directly at the conversion facilities, whereas the type of storage depends on the biomass characteristics and the climatic conditions. Additionally to the distance travelled or the quantity of biomass, the transporting cost is influenced by the number of rail or truck used and the actual routes taken by the vehicles. A representative fraction of Portuguese primary forest biomass is used for energy production, followed by wood-based products and pellets industry. Wood chips, pellets and briquettes are used for bioenergy production. When compared, the pellets have better balance concerning heating value (17 to 19MJ/kg), energy density (11000 to 13000MJ/m3) and price (150 to 200€/ton). Total harvesting costs vary between 20 and 25€/m3, depending on the level of mechanization felling. Load transportation costs can reach a value of 16.6€/m3.

ABSTRACT. This work deals with a novel renewable polygeneration plant based on solar and geothermal energy, producing simultaneously electric, heating and cooling energy. The developed plant consists of a 25 m2 photovoltaic field, a 45.55 kWh lithium-ion battery, a 6 kWe organic Rankine cycle, a 200 kWt biomass auxiliary heater, a geothermal well at 96°C and a 17 kW single stage H2O/LiBr absorption chiller. The organic Rankine cycle is mainly supplied by the geothermal well and produces a part of the electricity of the plant, the rest is produced by photovoltaic panels. A ground-cooled condenser is adopted in order to reduce the condensation temperature of the Organic Rankine Cycle, increasing the electric efficiency. The dynamic simulation model of the plant was accurately developed in TRNSYS environment, using libraries validated versus literature and experimental data. The organic Rankine cycle is modelled by zero-dimensional energy and mass balances implemented in Engineering Equation Solver and included in TRNSYS. The model of the renewable polygeneration plant is applied to a suitable case study, a commercial area near Campi Flegrei (Naples, South Italy), well-known location for its geothermal sources and good solar availability. The proposed plant exhibits promising energy performance achieving a primary energy saving of 51%. For the selected plant, a thermo-economic optimization has been implemented varying PV and battery capacities. The results suggest that the optimum configuration consists of large photovoltaic fields and small electric energy storage system capacity, due to the high specific cost of lithium-ion battery.

https://youtu.be/DlpfYR6R-7E

ABSTRACT. The companies’ performance effectiveness and sustainability, two imperative indicators of present-day business competitiveness, were partly and separately measured during many years. On one hand, the performance effectiveness has been measured through productivity, quality, cost, and other key performance indicators or a combination of them. On the other hand, many companies recognized the need for disclosure and begun to measure, key environmental performance indicators. Nevertheless, few studies point out a global indicator that could simultaneously tackle both strands, which in turn would enable a clearer assessment and endorsement of trade-offs among environmental and operational performances. This paper aims to present one such indicator, which measures the companies’ lean-green compliance, by interweaving sustainability issues with those of the overall equipment effectiveness (OEE). The authors termed it Business Overall Performance and Sustainability Effectiveness (BOPSE) indicator. Its purpose relies on assessing the business effectiveness, based on both operational performance and sustainability compliance. While the performance effectiveness is appraised based on a well-established indicator, the authors felt the need to develop, adapt and simplify the particular indicators aiming the assessment of sustainability compliance. The later is mostly sourced, adapted and simplified from the Global Reporting Initiative (GRI). This development was contextualized in a Lean-Green environment; where continuous efforts to uncover and reduce all the Lean waste sources, plus the waste prevention perspective of cleaner production, environmental compliance and social responsiveness, play a major role in the development of the factories of the future. This paper provides a discussion on the development of the BOPSE model and a comparative analysis of the ones found in the literature while providing insights into its relevance and wider application.

ABSTRACT. Significant research efforts have been invested in the automotive industry on hybrid-electrified powertrains in order to reduce the passenger cars’ dependence on oil. Powertrains electrification resulted in a wide range of hybrid vehicle architectures. Fuel consumption of these powertrains strongly relies on the energy converter performance, as well as on the energy management strategy deployed on-board. This paper investigates the potential of energy consumption savings of a serial hybrid electric vehicle (SHEV) using an hydrogen PEM fuel cell (PEMFC) as energy converter instead of the conventional internal combustion engine (ICE). A PEMFC model was developed and thermodynamic system efficiency was simulated. A SHEV model is developed and powertrain components are sized considering vehicle performance criteria. Energy consumption simulations are performed on WLTP cycle using dynamic programing as global optimal energy management strategy. Results show improved efficiency with PEMFC as auxiliary power unit (APU) compared to ICE. Moreover, PEMFC offers other intrinsic advantages such low noise and vibration, suitable vehicle integration as well as a zero CO2 tank to wheel emissions. The production of clean hydrogen is also discussed in this paper. Consequently, the studied PEM-APU presents a potential for implementation on SHEVs.

ABSTRACT. YouTube link: https://www.youtube.com/watch?v=5dFA8kbe8t4&feature=youtu.be

Coal-fired utility boiler is an important component of thermal power plant because increasing boiler efficiency leads to the reduction of the power plant heat rate. Boiler efficiency can be increased by decreasing the temperature of flue gas before it is exhausted to the environment. For a boiler that uses coal with high moisture content as fuel, two methods of recovering heat from flue gas are increasing surface area of air heater and installing flue gas dryer after air heater. The first method increases air temperature before combustion, and the second method reduces fuel moisture content before combustion. Both methods can result in the identical enhancement of boiler performance. However, previous investigations have not assessed the performance of the second method in comparison with the performance of the first method. In this paper, models of utility boiler and flue gas dryer are developed for comparative assessment of both methods. It is found that, in order to increase the boiler efficiency of a reference boiler to a specified value, the additional air heater surface area in the first method and the amount of moisture removed from fuel in the second method can be determined. Although both methods lead to the same boiler efficiency, the total installation costs of both methods may be different depending on the unit costs of flue gas dryer and air heater surface area. The installation of flue gas dryer has a cost advantage over the installation of additional air heater surface area if the ratio of the unit cost of flue gas dryer to that of air heater surface area is less than the upper limit.

ABSTRACT. Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads of rooms. Such system, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation are the most widely used strategies to determine the optimal level of supply air volume. However, many conventional CO2 mass balanced models assume equilibrium condition and may cause a time delay compared to the ground truth in determining the occupancy level. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by using machine learning techniques to predict real-time occupancy as the active constraint. The experiment was taken in an auditorium located on a university campus. The HVAC system can be described as a single zone all-air system having a constant air flow. The result shows, after utilizing a new supply fan schedule, a maximum of 40% fan energy reduction can be achieved.

ABSTRACT. Decarbonization and emission reduction from road transport are the main drivers for the electrification of the vehicles. The European regulation for CO2 emissions plans by 2030 a significant reduction to 50 g CO2 emissions per kilometer. This target already requires a massive market introduction of electrified and electric vehicles. Users expectation are the same as for the conventional vehicles: range adapted to specific use cases, usage comfort as good as the state of the art internal combustion engines -powered vehicles (availability, “re-fuelling” time and possibilities, passenger comfort…), safe parking and infrastructure also with direct connection to charging facilities. The management of the recharging of the batteries for the users is a central question. This article presents an innovative recharging service for the high voltage batteries of the electrified vehicles, provided by a robot. An innovative mobile robot, equipped with energy power system, does the recharging task. The navigation function is central in the design of the mobile robot. The navigation system must create a safe path planning to the vehicle and then ensure an accurate positioning of the mobile robot and safe navigation in its environment. The Localization and Mapping are the fundamental keys to provide the mobile robot with autonomous capabilities. The article presents a recent development and it experimental validation, in the Simultaneous Localization and Mapping method to serve the robot navigation, coupled with an advanced cognitive system. The proposed navigation system uses advances in Mobile Robot Localization and Mapping methods and a LiDAR sensor. The cognition uses deterministic algorithms to fulfil the positioning accuracy, the low cost of the equipment, and easy implementation requirements for industrialization. The experimental results on a mobile robot platform show a distance error lower than 0.7% and an angle error less than 0.5%, promising for the acceptable accuracy of the system.

ABSTRACT. Youtube presentation: https://youtu.be/vDCeUQKEmZE

In order to extend the lifecycle of industrial assets and optimize the use of material, energy, economic and human resources, there is a proposal to establish geothermal power plants in abandoned oil wells. In this context, an analysis was made of the deployment of Organic Rankine Cycles (ORC) in four abandoned wells in Brazil. These study cases are located in the states of Mato Grosso, Paraná, Rio Grande do Norte and Santa Catarina and have vertical depths between 4102.8 m and 6053.5 m. The characteristics of the heat source that feeds the power cycle were taken from the results of thermal simulations of these wells available in previous works, which contains estimated data about the rate and conditions of hot fluid extracted from the well over 30 years of activity. The ORC was designed with a single stage turbine and no heat recovery. The ambient conditions were taken as constant and corresponding to the annual averages for the respective localities. This information was inputted to optimize the saturation pressure and temperature in the Evaporator for the design point, which resulted in nameplate electrical power between 10.7 and 13.7 kW. After defining the design point of each power plant, the mass flow of R245fa in the organic cycle was estimated, aiming at the maximum generation of electricity year by year. From this, a nonlinear regression was defined whose function defines the net electric power as a function of time, which obtained coefficients of determination greater than 0.9994. The integration of this generated function over time made it possible to estimate the net electricity produced annually. In addition, other outcomes of the power cycles were evaluated, for example the thermal efficiencies, which range from 3.46% to 5.33% for the nameplate condition.

ABSTRACT. Access to affordable and clean energy is one of the basic requirements for sustainable growth. Bangladesh, being one of the developing nations, currently face a rapid growth in the electricity sector. However, this growth might cause energy security issues as natural gas, the main source of energy and electricity is depleting quickly and might be totally depleted by the next decade if no new gas-fields are discovered. Local and imported coal could be alternative to natural gas, which causes environmental pollution by emitting carbon di oxide and other green house gases. Nuclear and renewable energy sources are also considered as future solution to the energy security problem though they have their specific technical limitations.

In this paper, we consider all available and potential energy sources for electricity generation taking into account their techno-economic limitations and apply the linear programming method to obtain the best generation mix which would ensure low cost and limited emission at the same time. The whole country is geographically divided in to nine regions to obtain high spatial resolution. In addition, hourly demand for all different nodes is projected so that high temporal resolution could be achieved for best optimization. This dynamic optimal power generation mix model provides optimized generation and capacity mix from 2020 to 2050 at five years interval. Different policy scenarios including carbon-emission limit and adaptation of new technologies has been considered for sensitivity analysis. This analysis provides a clear guideline for low-cost optimum electricity sector expansion planning for developing countries like Bangladesh which also promotes sustainable development.

ABSTRACT. YouTube Video Link: https://youtu.be/TmUASpT6I2Y

The rising share of Variable Renewable Energy Sources (VRES) in the electricity generation mix leads to new challenges for the whole energy system. It especially raises technological issues to handle variability and to match electricity load with supply at all times. This study introduces a new methodology to quantify the relevance of different electricity storage technologies, based on a time scale analysis. It additionally provides an understanding of how electricity storages work in combination to handle variable load and intermittent generation. First, we set up a simple model of variable production, fluctuating over a single time-scale. This analysis gives figure of merit for electricity storage and curtailment. Second, we simulate the collaboration and competition behaviour of various storages with a dual time-scale signal. Then, results are compared with the optimization of an energy system with real variable electricity supply and consumption time-series. We eventually highlight the trade-of mechanisms between the storage efficiency and its investment cost.

ABSTRACT. Efficiency and sustainability play an increasingly important role in most industrialized countries. Within the context of building energy systems the integration of renewables represents a key challenge. As these forms of energy show very volatile characteristics, local energy systems need more flexibility regarding the time of energy purchase. There are numerous approaches in research for the optimal use of storage units, such as rule-based control, model predictive control or adaptive control. However, most of these methods depend on detailed models of the system dynamics. A major hurdle to the manual development of physical white-box models for building energy systems is the low investments possible in most countries due to low energy costs. Furthermore, the creation of such models is very time-consuming and error prone, even for domain experts. Another weakness is, that changes in the systems are not automatically adapted within the models. However, with the steadily increasing availability of computing power and collected data in recent years, the practical application of methods from the field of machine-learning has yielded increasingly good results. Machine-learning methods can help to obtain data-driven, self-calibrating models, which can be learned from operation data directly. In this paper, we apply methods for automated data-driven model generation. We demonstrate how these techniques can be used to model individual subsystems as well as a complete energy supply infrastructure. The considered system is integrated into a district cooling network and consists of two compression chillers and an ice storage unit. This work is part of an ongoing research project with the aim to optimize the operation of the entire system. Our results suggest that, if detailed monitoring data is available, data-driven modelling represents a viable alternative to the labor-intensive white-box modelling approach.

ABSTRACT. Colombian mining sector is characterized by the production of coal, nickel, emeralds, gold and construction materials. According to the latest National Development Planning report, mining sector plays an important role as an economic agent that drives the economy and development in the region, even though mining activities still require a strengthening of its environmental responsibility policies [1, 2]. Thus, this work aims to show the importance of applying methodologies based on physical laws (exergy method) that objectively evaluate the impact of extractive gold activity, such as alluvial mining. The mining process begins with the extraction of gold out from gravel, clays and sands by using of spoon dredges, wherein the gold content is separated by gravimetric processes depending on its weight and size, without requiring the use of mercury. The evaluation of the extraction process will be performed through an exergy analysis, at both plantwide and unitary process level in order to determine the exergy efficiency of the system and its components and spotlight potential improvements. Actually, the higher the exergy efficiency, the lower exergy destruction, and therefore the lower the environmental impact associated to the atmospheric emissions. This analysis can be interpreted as a preliminary guide for decision-makers in the planning of extractive processes, as it integrates the environmental component within the production assessment.

ABSTRACT. Offshore oil and natural gas production is an energy-intensive activity and is responsible for the emission of significant amounts of carbon dioxide into the atmosphere. The main emitting source is the open-cycle gas turbines (OCGT) of the utility system which supplies heat and power to the production platforms. Severe vessel area and weight constraints are often cited as the main reason why production platforms are unable to receive high-efficiency combined-cycle gas turbines (CCGT) common in land-based power plants. Published work suggests that in production development projects of giant offshore oil fields, the thermodynamic efficiency of the utility system may be increased significantly, without prejudice to project economic viability, through an additional vessel dedicated to generating power in CCGT. The best results were obtained with the power hub associated with heat and power production with OCGT located in the production platforms. Therefore, this work proposes a methodology for optimizing the configuration of the utility plant in a scenario with a power hub and multiple production platforms. The first step is the selection of combined cycle configurations from the commercially available aero-derivative gas turbines. At sequence, evolutionary algorithms are used in the multi-objective optimization (MOO) of the steam bottoming cycle, whose objective is to obtain the configurations that produce the best results in terms of atmospheric CO2 emissions, occupied area, and capital cost. A method is then proposed to select the best solution from the non-dominated solutions that compose the Pareto front, taking into account the constraints imposed by the vessel of the central power plant and the objectives to be optimized. Finally, in the context of growing environmental concern and taxation of CO2 emissions, this work contributes to highlighting the advantages of the central power plant in future maritime production development projects in large oil and gas fields.

ABSTRACT. An offshore central power station installed on the deck of a decommissioned and adapted floating, production, storage and offloading (FPSO) unit is projected for supplying the electricity required along the lifetime of four identical FPSOs, aiming to increase the efficiency and alleviate the environmental burden of offshore oil and gas activities. In face of the conflicting targets (e.g. weight, area, cost and efficiency), intrinsic to offshore power generation systems, along with prolonged offdesign operating conditions, a combined thermodynamic, environmental and economic analysis is used to optimize the performance of the power hub proposed. Thus, this work quantifies the space and weight allowance and sheds light on the feasibility of offshore central power stations with carbon capture units, installed on decommissioned FPSOs. As a result, these advanced setups may provide higher overall power generation efficiencies (42%) than existing open cycle gas turbine (OCGT) configurations (about 30%), even at tenfold lower CO2 emissions. Lastly, the peak of energy demand to the power hub, the initial investment and the modular flexibility of the hub are briefly discussed on light of the delay on entrance in operation of each petroleum platform.

ABSTRACT. Youtube Video Link: https://youtu.be/21O4TQAHqe8

Biogas is a promising renewable and distributed source of energy derived from the anaerobic treatment of organic residues. Although this biofuel can provide substantial benefits for the environment, its application may be restricted to large industrial facilities due to the lack of efficient conversion systems at low production scales. A possible solution is the use of high temperature fuel cells, such as solid oxide fuel cells (SOFC), to directly convert biogas into electricity, heat and syngas. Besides the expected increase in power efficiency, this technology could also provide an alternative source of hydrogen by purifying the anode exhaust gas. However, biogas reforming may impose new design challenges to these polygeneration systems which were seldom studied. Moreover, the influence of operational parameters in the trade-offs between efficiency and equipment size were also rarely investigated. Thus, in this research work, a thermodynamic model for a biogas fuelled SOFC with internal reforming and hydrogen co-production is proposed and analyzed. The results indicate that, depending on the operational conditions, an exergy efficiency of 52-76% and a net power density of 1817-4148 W/m2, could be achieved by a fuel cell system co-producing electricity and hydrogen. In general, hydrogen production increases the exergy efficiency with minor losses in the net power density, which may lead to solutions with higher economic viability. The energy integration analysis indicated the possibility of waste heat recovery, while the exergy destruction analysis pinpointed major losses in the heat exchanger network (51-53%), fuel cell (16-17%) and catalytic burner (14-16%)