Net Present Value Research Articles

Fine-tuning ocean energy storages for reservoir-integrated wave energy converters with dynamic activation of mechanical energy storage features

The proportion of renewable energy in the power supply has experienced substantial growth; however, its intermittent nature and inherent uncertainty present notable challenges in its integration. This study aims to explore the significance of energy flexibility in energy management by focusing on a hybrid renewable wave-wind zero-energy system implemented in a coastal building. Specifically, the study investigates various sources of flexibility within the system and introduces a novel energy storage mechanism utilising the in-built reservoir of the wave energy converter. The research provides new insights into the flexibility of control strategies from the generation and storage sides. The cost-responsive controls involved on/off-peak periods and monthly variations. Several attempts have been made to develop cost-responsive controls that depend upon the electricity tariffs; these include energy shift and peak shaving. The lower discharging limits and trigger lines were compared in the simulation environment to improve the cost-saving, energy-matching, and peak-shaving performances. In this study, the first group scheduled discharging; however, only limited improvements (0.5%) were achieved. Consequently, the second group shifted to varying the minimum fraction state of the reservoir under a lower discharging limit, which reduced operational cost by 4.5%. Using different discharging lines by considering the current month and the period, the third group achieved the expected peak shaving and correspondingly reduced the operational cost to 90.3 %. The final group showed that an additional battery could improve the peak-shaving and cost-saving performance but not the net present value, owing to high investment costs. Furthermore, annual savings increased for different battery capacities from 4.5 % to 6.8 % by conducting the energy shift. Peak shaving control with additional battery reduced maximum demand and substantially reduced costs from 6.4 % to 10.8 %. The study recommends focusing on peak shaving with extra batteries for further cost savings during the on-peak period. This research brings novelty by integrating flexibility control for both generation- and storage-sides in ocean renewable energy systems. It proposes using a wave energy converter as a mechanical energy storage reservoir, reducing costs and ensuring adequate capacity. The study emphasises dynamic storage control, adjusting in real time based on conditions and energy demands.

Driving industrial symbiosis: Evaluating policy intervention effects on waste heat utilization in local district heating networks

This study aims to quantitatively assess the required policy support level to ensure economic viability for all stakeholders in the context of using industrial waste heat as source of local district heating (DH). Additionally, the research explores the potential of industrial symbiosis to support district-level energy transition in the context of Positive Energy Districts and Smart Cities. The proposed approach combines energy modelling tools, namely the Multi Energy System Simulator (MESS) model and financial concepts (i.e. Cash Flow Analysis, Net Present Value Evaluation) typical of industry’s project evaluation. It is then tested in the area of Bolzano, Italy. The study examines various policy scenarios, including financial incentives and the involvement of district heating operators (DHOs). Initial findings indicate that district heating offers significant cost advantages (up to –23%) over electrified systems. However, industries face challenges meeting standard payback periods without policy intervention in the context of projects using waste heat as a source of DH. A combined scenario involving DHOs handling connection costs with a 60% incentive and industries receiving a 15% Feed-In Premium shows promising results in achieving economic feasibility. The results underscore the critical role of policy support in overcoming barriers. Consequently, it is possible to strengthen cooperation between industries and districts fostering local energy transition. Nonetheless, the achievement of positive energy balance requires a stronger presence of RES particularly on the electricity side. The study highlights the broader implications for similar projects in other regions and the essential role of policy interventions in facilitating such industrial symbiosis.

ANALYSIS OF THE ECONOMIC PERFORMANCE OF PROJECT ADVANCED OIL RECOVERY WITH CO2 INJECTION (EOR/CCUS-CO2) THROUGH FINANCIAL INDICATORS: BRAZILIAN OVERVIEW

Enhanced Oil Recovery (EOR) is a method associated with Carbon Capture, Utilization, and Storage (CCUS) that consists of injecting CO2 into mature oil fields to store CO2 and enhance production. CO2-EOR projects are costly, requiring technical and economic evaluations to determine their viability. This research analyzes various economic parameters in CO2-EOR projects to develop a comprehensive model for assessing their economic viability in Brazil. The model was tested on projects developed by Silva et al. (2010) and included six scenarios of CO2-EOR in mature reservoirs. The results demonstrate the impact on well completion costs. The scenario with the highest oil production had one additional completion and, for this reason, did not show the best Net Present Value (NPV) result. This work also conducts a sensitivity analysis of parameters and reviews their impacts on NPV. Results indicate a significant impact of barrel price and minimum attractiveness rate on the NPV, while finding that the taxation of excess CO2 was not very significant. Low barrel prices (<$9) proved to be unfeasible with negative NPVs.

ECONOMIC FEASIBILITY STUDY FOR THE IMPLEMENTATION OF A MICRO-INDUSTRY PRODUCING KOMBUCHA FLAVORED WITH PASSION FRUIT FROM THE CAATINGA.

Kombucha is a fermented beverage of Chinese origin produced from the infusion of black or green tea from the Camelia sinensis plant, sugar and a symbiotic culture known as SCOBY. After fermentation, the drink becomes rich in natural nutrients with functions that are beneficial to health such as organic acids, amino acids, vitamin C, polyphenols and antibiotic substances. The present work aims at carrying out a study of the economic feasibility of opening a micro-industry for the production of a black tea kombucha flavored with passion fruit from the caatinga. The caatinga passion fruit is a species native to the dry areas of northeastern Brazil. This fruit is rich in flavonoids, vitamin C and carotenoids, which have antimicrobial and antioxidant properties. For the development of the study, specifications of processes, equipment and analysis of financial and economic indicators were carried out. The evaluation of the investments was carried out using the internal rate of return (IRR), minimum attractiveness rate (TAM), net present value (NPV), simple payback and discounted payback methods. The initial investment for the production of 2,000 L of kombucha flavored with passion fruit from the caatinga was R$ 187,199.78. The economic indicators obtained resulted in NPV of R$ 253,154.68, IRR of 22%, TAM of 13.34% p.a. and simple payback of 3.9 years considering a useful life of 10 years. The positive IRR reflects that the enterprise is economically viable and profitable, making it possible to invest and generate income for cooperatives and family farmers.

Cost benefit analysis of reducing speed limits in Athens to 30 Km/h

The objective of this research is to investigate public acceptance and socio-economic feasibility of reducing speed limit from 50 km/h to 30 km/h in certain parts of the road network of Athens, Greece. A questionnaire was developed based on the method of stated preference for various hypothetical scenarios of time, fuel consumption and the probability of road crash to investigate road user preferences towards the reduction of speed limit and identify the most important influencing factors preferences. A total of 408 respondents were asked to choose among three alternative proposals: a) Reduce the speed limit to 30 km/h throughout the urban network except on major arteries, b) Reduce the speed limit to 30 km/h throughout the urban network and c) No Reduction (do nothing). For the analysis, two binomial logistic regression models and two multinomial logistic regression models were developed. The results indicate that increase in travel time, the importance of speed in causing a crash, the number of crashes the user has been involved in, the respondent’s driving habits are the main determinants of the users’ preferences. Furthermore, a Cost Benefit Analysis (CBA) was conducted to understand the sustainability implications of the implementation of lower speed limits (i.e., 50 to 30 km/h). In terms of socio-economic impacts, the CBA considered safety-related metrics, namely the number of road casualties of all severity levels, and environmental-related metrics, namely reductions in CO2, NOx, PM levels as well as in road user surplus metrics namely travel time increase and fuel consumption reduction. Costs considered in the analysis result from increased time spent on the road. Consequently, the investment and the operational costs along with the socio-economic impact are estimated and monetized, up to the year 2030. The positive Net Present Value (NPV) and the high Internal Rate of Return (IRR), i.e. 64.5 %, obtained as well as the sensitivity analysis results, indicate the feasibility of this policy over time. Therefore, speed limit reductions are economically viable and so, should be encouraged.

Fabric Retrofit of a Hard-to-Treat, Pre-1919 House in Preparation for Heat Pump Use

The uptake of low-carbon domestic heating systems is a significant strategy towards global targets of reducing greenhouse emissions and mitigating climate change. Pre-1900 hard-to-treat houses will still be existing in the next 25 years, and they have the greatest potential for improved energy-efficiency. This study investigates the potential of fabric retrofit to prepare an older, hard-to-treat house type for heat pump use. The house type was modelled in DesignBuilder and validated using the Ulster University test house. The wall, loft and floor insulation, as well as glazing upgrades can yield up to 50% reduction in heating demand for a hard-to-treat house type, thereby preparing it for heat pump installation. Additionally, upgrading insulation and glazing in line with the current building standards was cost-effective, with a net present value of approximately GBP 12,000.

Optimization and exergoeconomic analysis of a biomass-driven cogeneration system for power and cooling applications

Due to the increasing population and the expansion of industry in daily life, a significant depletion of fossil fuel resources is observed, accompanied by escalating environmental concerns. In response to these challenges, this study presents an optimized cogeneration system powered by renewable biomass, designed for simultaneous power and cooling generation. The system integrates a biomass gasification unit, a steam Rankine cycle, a helium-driven gas turbine cycle, and an ammonia-water-based two-stage absorption refrigeration cycle with a bleed line. Thermodynamic and exergoeconomic analyses reveal a net power output of 5356 kW, a cooling load of 360.9 kW, and an exergetic efficiency of 29.52 %. Financially, the system shows a net present value of 11.76 $M and a payback period of 6.51 years. A multi-objective optimization utilizing the gray wolf algorithm improves system performance through the employment of three diverse scenarios, which raises power output to 5864 kW, cooling load to 372 kW, and optimize net present to 14.28 $M with a reduced payback period of 5.96 years. The studied system is well-suited for industrial applications, rural electrification, and district energy systems, and offers a sustainable and economically viable energy solution.

Time is Money: The Return on Investment of Research in Surgical Training: Brief Title: The ROI of Research in Surgical Training

IntroductionFuture income potential can impact surgical trainees’ career choices, particularly when deciding to subspecialize, which often requires additional training and research time. This study quantifies the effects of added time on career value for eight surgical subspecialties. MethodsThe Net present value(NPV) was calculated for eight subspecialties and general surgery over a 35-year career, factoring in salary, educational debt, tax, inflation, and practice setting. NPV for each was compared over a number of research years (0, 1, 2) using data from the MGMA, AAMC, and US government records. ResultsAfter a 35-year career, six subspecialties in private practice increased career NPV(>$14,000) with 0 research years. One additional research year yielded negative career values for transplant, trauma, and vascular; with two, only cardiovascular and pediatric retained a positive NPV. In academia, 1-2 research years resulted in negative NPV for all but cardiovascular and thoracic surgery. ConclusionsThe financial return of additional training years is highly variable.

An environmental cost-benefit analysis of organic and non-organic sheep farming in Iceland

This paper presents an environmental cost-benefit analysis (CBA) comparing organic and non-organic sheep farming in Iceland over three decades. Environmental life cycle costing (ELCC) is the basis for the aggregate cost framework, encompassing costs for investment, operations, maintenance, and environmental externalities. Greenhouse gas emissions are included as an external cost as they relate to the monetization of damages to society. Net present value (NPV) and benefit-cost ratio (BCR) indicators reveal both systems as unprofitable over thirty years with and without subsidies. Depending on the discount rate, the organic farm's BCR ranges from 0.26 to 0.38, with a NPV between (-2.4) and (-1.1) million euros. The non-organic farm's BCR ranges from 0.12 to 0.19, with NPV between (-8.6) and (-3.7) million euros. Greenhouse gas emission costs constitute 28% and 33% of total life cycle costs for organic and non-organic farms. Across three discount rates, the LCC and ELCC per kilogram of organic lamb ranged between €13.78 – 25.27 and €18.23 – 37.07, respectively. The LCC and ELCC per kilogram of non-organic lamb ranged between €9.75 – 18.05 and €13.80 – 28.82, respectively. Due to a shortened value chain, organic sheep farming demonstrated revenue advantages as compared to non-organic. The external cost of greenhouse gas emissions from sheep farming forms a significant portion of lifetime costs suggesting that policymakers could incentivize organic practices aimed at reducing greenhouse gas emissions. Finally, this study provides a deeper understanding of the long-term economic prospectivity of sheep farming under different production approaches.

The Finance of Cybersecurity: Quantitative Modeling of Investment Decisions and Net Present Value

Despite the growing literature on cybersecurity investment, significant gaps remain unaddressed. First, the existing literature does not differentiate between cybersecurity expenditures, namely investment versus spending, despite the differences in their financial treatments. Second, the literature predominantly employs economic and risk-based models, in which the incorporation of financial parameters is limited. Third, the literature focuses on quantifying market (stock) loss of cyberattacks or conducting a cost-benefit analysis of cybersecurity investments but has yet to bridge these two areas. Thus, our objective is to introduce a new financial model, the Alpha Model, that addresses these gaps. We first provide distinctions between cybersecurity investment (capital expenditure) and spending (operational expenditure). Grounded in Relative Valuation Models and Optimal Capital Structure Theory, we quantify the upper-bound of optimal cybersecurity investment by utilizing book and market values of equity and debt. We also demonstrate that the Net Present Values (NPV) of cybersecurity investment is considerably lower than its book value by incorporating financial parameters of equity and debt financing, inflation-adjusted capital cost, and tax credits of depreciation and interest. To validate our model, we empirically test it by using data from the S&P500 index during 2018–2023. We offer practical implications to key stakeholders, including: (1) industry executives to financially quantify cybersecurity investments and ascertain their actual NPVs, individually and collaboratively with supply chain partners; (2) cybersecurity solution providers to promote business investments in cybersecurity; and (3) insurance providers to price premiums with higher NPVs than the corresponding investments. We also offer several avenues for further research.

Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China

During CO2-EOR implementation, CH4 in the reproduced CO2-rich mixture are included in the recycle injection gas. However, improper injection gas composition and operation parameters can reduce the flooding performance.In this study, a machine-learning assisted framework combining Convolutional Neural Networks-Gate Recurrent Unit (CNN-GRU) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to obtain the optimal parameters for simultaneous maximum oil production, carbon storage efficiency, and net present value. A case study from the CO2-EOR project in the H59 block of Jilin oilfield, China, was carried out. Base cases were established to evaluate the effects of reinjection CH4 on flooding performance.Results show that the built CNN-GRU model has high prediction accuracy and computational efficiency, and thus can be used as an alternative tool to the reservoir simulator. The proposed framework can find the optimum parameters to improve oil recovery, carbon storage and net present value. The co-injection of CH4 and CO2 improves oil production, and carbon storage performance, but reduces the net present value. This work provides engineers with multiple strategies for decision-making to simultaneously promote flooding performance with CH4 being a co-injectant in CO2-EOR projects.

Comprehensive Analysis of 100 MW Wind Farm Losses and Their Financial Impacts: A Study on Array Losses Across Multiple Turbines Using a RETScreen Expert Software

Abstract This study aimed to analyze and evaluate the influence of array losses on the financial sustainability and economic viability of wind farm projects with a capacity of 100 MW. The Al-Fajer site has been proposed for a feasibility study to assess the viability of building an onshore wind farm. The assessment of investment costs was conducted using the RETScreen program. The findings demonstrated that alterations in array losses impact the amount of energy exported to the grid, influencing changes in revenue, pre-tax internal rate of return (IRR), and net present value (NPV). When array losses in (case 1) decrease by 2%, that will positively impact financial feasibility factors. Therefore, it will lead to a gain in income for all turbines; the net present value (NPV) and pre-tax internal rate of return (IRR) values experienced an increase, indicating a positive impact on the project’s profitability. When array losses in (case 2) increase by 2%, it will lead to negative results on the wind farm and, thus, reduce the energy exported to the grid; wind turbine revenue will experience a decline. This increase substantially affects the NPV and IRR, leading to decreases. The capacity factor experienced a drop, resulting in significant changes to the project’s financial returns. The levelized cost of energy (LCOE) has increased due to decreased production, leading to higher prices. The simple payback likewise experienced a boost beyond its usual norms.

ANALISIS KELAYAKAN HARGA JUAL DALAM PERSAINGAN PASAR PRODUK SYNTHETIC THICKENER

This study aims to analyze the feasibility of the selling price of synthetic thickener products in tight market competition based on financial aspects of Net Present Value (NPV), Benefit Cost Ratio (BCR), and Profitability Index (PI) with three price levels from the price range requested by customers. The research was conducted using a qualitative research method with five stages, namely 1) defining the problem, 2) identifying each alternative, 3) analyzing the feasibility of the selling price with a three-level price scenario, 4) conducting a comparative analysis and 5) recommending the decision given. The results of the research with the NPV, PI and BCR methods are not feasible for price as well as level 1 and feasible for selling prices 2 and 3.

Production-Enhancement-Candidate Screening Powered by ML Unlocks Brownfield Potential

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215244, “Automated Production-Enhancement-Candidates Screening Powered by Machine Learning Unlocks Untapped Potential in Matured Oil Fields: A Case Study,” by Nusheena M. Khair, SPE, M. Farid Zaizakrani, SPE, and Nur A.I.Z. Azhar, Petronas, et al. The paper has not been peer reviewed. _ Field A offshore East Malaysia has been a productive conventional oil field for more than 3 decades. It has encountered several surface and subsurface issues, including sand production, increasing water cut, and reservoir-pressure depletion. The complete paper presents the process of identifying production-enhancement opportunities, develops a methodology to identify underperforming candidates and analyze well-integrity issues, and describes how data science and engineering analyses are integrated. Introduction Field A is undergoing redevelopment, including activities such as enhanced oil recovery, production-enhancement (PE) initiatives, idle-well reactivation (IWR), and infill drilling. However, Field A’s operations and PE initiatives face multiple challenges. These include a shortage of comprehensive data affecting reservoir management and forecasting, difficulties in managing wells on unmanned platforms during adverse weather, and high logistical costs in offshore interventions that affect the economic feasibility of certain PE and IWR jobs. Recognizing these issues, the operator team is taking a proactive approach by embarking on enhancing PE and IWR candidate generation by machine learning (ML), a strategy that aims to expedite well-by-well review (WBWR) to streamline candidate-generation processes and enhance decision-making. Production Enhancement Candidate Generation and Screening (PECGS) Methodology PECGS is a petroleum engineering advisory system that helps operators improve the efficiency of PE and IWR candidate screening and intervention planning. PECGS automatically generates a list of underperforming wells along with recommendations for remedial actions. PECGS works by first evaluating each well’s production potential and risk assessment through use of a data-driven advisory system that combines analytical and ML models with industry-standard petroleum and reservoir management logic. Once the wells have been evaluated, PECGS generates a live automated opportunity register with a ranking of candidate wells. Technical Solution The technical solution in PECGS includes four main stages: knowledge base, technical analysis, chance of success, and economic analysis. The knowledge base of PECGS is formed through data integration from various sources. Static data have been standardized and validated by subject-matter experts before being uploaded into the advisory-system database. Dynamic data, such as production and well test, are integrated daily into the advisory system database according to a predefined schedule. The technical analysis is leveraged with proven analysis methods to identify well constraints and recommend PE opportunities and present estimated production gain, success probability, and profitability. The workflow uses multicriteria ranking, or the analytical hierarchy process (AHP), to rank screened wells based on production and petrophysical key performance indicators. Once a potential PE candidate has been identified through an automated screening, the next step is to review the chance of success. Next, the engineers perform an economic analysis to evaluate the potential profitability of the PE job. The economic analysis uses a variety of factors, including the cost of the intervention, the expected increase in production, and the value of the produced oil or gas in order to calculate the net present value (NPV) of the job. If the NPV is positive, then the intervention is profitable.

Life cycle assessment (LCA) and economic analysis of two-stage anaerobic process of co-digesting palm oil mill effluent (POME) with concentrated latex wastewater (CLW) for biogas production

This study outlines the results of a process-based life cycle assessment (LCA) of the environmental performance of biogas production from co-digestion of palm oil mill effluent (POME) and concentrated latex wastewater (CLW) in the two-stage anaerobic digestion (AD) process. Biogas is utilized global across several sectors, including electricity generation, heating, and transportation. The Open LCA 2.0.2 software was used to evaluate the LCA at the midpoint level by using 18 different impact categories. This study reveals the three main contributions of the AD process, which were global warming (8.65 × 102 kg CO2eq), water consumption (5.32 m3), and land use change (3.39 × 102 m2 a crop eq), this result corresponded with the previous study in Malaysia that examined biogas production from the AD of POME In addition, alternative scenarios, in which a single substrate, have been established to quantify the impacts of potential improvements of biogas production to compare with base scenario of co-digestion. The results show that the environmental impacts of the base scenario released were lower than those of the single POME substrate of alternative scenario 1 in most impact categories. Furthermore, economic analysis is a crucial factor in the practical implementation of the AD process. Linking LCA with economic analysis offers a comprehensive perspective on sustainability, balancing environmental impacts with financial performance to optimize the potential of the AD co-digestion process. This is the first study to apply LCA and economic analysis to this specific co-digestion process in a two-stage AD system. The economic analysis of the two-stage AD of co-digesting POME and CLW was 1200 m3/d production of wastewater (70 % of POME and 30 % of CLW) based on the factories' data. The result indicates net present value, internal rate of return, and payback period of 515,813.94 USD, 9.13 %, and 8.87 years, respectively. This work proposed the potential approach for implementing the two-stage AD co-digestion of POME and CLW while concurrently producing valuable bioenergy carriers.

Furfural purification and production from prospective agricultural waste of oil palm empty fruit bunch: Simulation, design and economic assessments

Furfural is potentially produced from lignocellulose waste of biorefinery processes and is widely used as a value-added in various chemical industries. However, the purification of furfural should be conducted to obtain high purity. This work aims to synthesize, design, and optimize the furfural production using some alternative distillation processes by simulation using Super Pro and ASPEN software. The pretreatment process of producing crude furfural from empty fruit bunch waste is also evaluated. The production cost of $0.23/kg of crude furfural (5%) was obtained in the preliminary process. In the purification process, the sequenced distillation process was less prospective than the extractive distillation based on the simulation basis and economic evaluation. The extractive distillation using n-butyl chloride performed better than toluene and benzene as the furfural recovery, and the purity was 98.60% and 99.94%, respectively. The payback period (PBP), internal rate return (IRR), and net present value (NPV) also indicated the great performance of the extractive distillation process with values of 1.24 years, 36.04%, and $14,591,500, respectively. Therefore, the simulation, design, and economic evaluation presented promising results that are feasible for plant establishment.

Extended material requirement planning (MRP) within a hybrid energy-enabled smart production system

A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy can contribute to Net Zero Emissions. The logistics framework involves an autonomation technology-based production system, optimum cash flow, logistics, and carbon emissions. Time is an essential influencer for material requirement planning. The model is solved with a Laplace integral transformation, where an associated matrix method is utilized by the input–output analysis. The theoretical concept is elaborated through an illustrative numerical example, where the energy consumption and corresponding net present values are evaluated. Numerical and graphical studies prove the effectiveness of the model for the use of renewable energy within for material planning under a reverse logistics system. The result reveals that efficient renewable energy consumption can save considerable costs and reduce the negative net present value of the system. It is found that skilled workers are worthy of a smart production system, not only in a qualitative aspect but also in an economic aspect.

Phased optimization of multi-thermal fluid flooding for enhanced oil recovery

Multi-thermal fluid flooding has become a critical technique in heavy oil reservoirs and is widely applied in China. Owing to the complexity of the displacement process and the involvement of multiple injection media, conventional optimization methods are unable to achieve economic and efficient development of heavy oil. In this study, an efficient phased parametric optimization method for multi-thermal fluid flooding was developed using horizontal wells in heavy oil reservoirs. This method utilizes particle swarm optimization (PSO), which targets the net present value for the phased optimization of multi-thermal fluid flooding. A comparison between the phased optimization and non-phased optimization shows that the cumulative oil production increased by 16.74% and the net present value increased by 39.66% with phased optimization. The effectiveness of the phased optimization method was verified using a field model. Optimal injection parameter charts were developed for each phase of the multi-thermal fluid flooding under different reservoir parameter conditions. The charts illustrate that the optimal gas injection rate and chemical mass concentration tend towards zero during the steam flooding initiation phase under varying reservoir parameter conditions. Conversely, all the media injection rates increased during the thermal fluid control and steam breakthrough phases. Additionally, with an increase in the crude oil viscosity, effective formation thickness, and original oil saturation, the optimal injection rate of the various media increases. However, with an increase in the permeability and reservoir depth, the optimal injection rate of the various media decreases.

Techno-Economic Analysis of Utilization Offshore Platform in the Java Sea to Produce Hydrogen with Offshore Wind Turbine-Based Energy Sources

Abstract Indonesia’s geographical condition, which consists of 70 percent ocean territory, holds the potential for utilizing offshore wind turbines to provide cleaner energy. In this study, economic calculations were conducted for offshore wind turbines to generate green electricity and green hydrogen. The chosen ocean area for this endeavor is around the Papa offshore platform in the Java Sea. This area has experienced an economic decline, making it viable for repurposing as a sub-station for a powerhouse and an electrolyzer plant. Based on meteorological data analysis results, the area around the Papa offshore platform exhibits an average at height of 119 m; the wind speed is 5.5 m/s.The prevailing wind direction is East Southeast (ESE), with a frequency of 25.2%. Further analysis of wind farms aimed at generating a capacity of 100.8 MW revealed an optimal potential energy generation of approximately 141,656.9 MWh per year. For scenario 1 (electricity production), the cost is $0.192/kWh. The levelized cost of hydrogen (LCOH) for scenario 2 (offshore electrolyzer) and scenario 3 (onshore electrolyzer) is $9.44/kg and $10.1/kg, respectively. To assess investment feasibility, a Net Present Value (NPV) analysis was conducted. This analysis yielded a selling price of electricity at $0.0271/kWh for the first scenario. For the second scenario, the selling price of hydrogen was calculated at $17.11/kg, and for the third scenario, it was $16.99/kg. Considering its flexibility and potential response to restrictions, the third scenario was deemed the most suitable for offshore utilization to produce clean energy.

Development of a Novel Process Towards an l-Malate Biorefinery Using Methanol as Feedstock

Malate is primarily obtained from fossil resources. However, growing environmental concerns are rerouting chemical manufacturing to biomanufacturing. Despite significant ‘discovery research’ efforts for sustainable malate production, a successful industrial implementation remains elusive. A novel methanol-based l-malate biomanufacturing process using the methylotrophic bacterium Bacillus methanolicus is designed and the relationship between R&D and techno-economic feasibility is assessed. Two scenarios are modeled and compared using SuperPro Designer®. First, with limited R&D success a 65g/L titer is achievable. Then with further strain improvement and process development titer is increased to 100g/L. Four independent design parameters; biomass formation, CO2 evolution, methanol loss and selling price are selected to analyze techno-economic metrics of interest (yield per batch, yield on methanol, unit production cost, and net present value). Sensitivity analysis reveals the dependence of techno-economic feasibility to R&D success, while uncertainty analysis quantifies how uncertainty in process development propagates into uncertainty in process performance.