Purchase Cost Research Articles

Super-resolved fluorescence imaging utilising accessible stochastic optical reconstruction microscopy (easySTORM) implemented on a low-cost, modular open-source (openFrame) microscope

Abstract Optical super-resolution microscope is a powerful tool for the life sciences, including cell biology and pathology yielding information on, e.g., biological structure and protein distribution in the nano-scale range. In recent years, there has been an exponential rise in the interest to apply super-resolution microscopes in diverse areas, but its wider utility is hindered by the cost of purchasing and maintaining super-resolved microscopes. In this paper we present the implementation of easySTORM, an accessible implementation of stochastic optical reconstruction microscopy (STORM) at Indian Institute of Technology Guwahati (IITG), India, in a flexible, user friendly and cost-effective manner using a state-of-the-art, modular, open-source, optical microscope platform called: "openFrame". Providing comparable imaging performance to commercial optical super-resolution microscopes, the openFrame-based implementation of easySTORM uses in-expensive multimode diode lasers and industry grade CMOS cameras, and the open-source and modular nature of the instrument makes it easy to maintain and to upgrade. To demonstrate its successful implementation at IITG, we image quantum dots and actin-tubulin structure in both normal and cancer cells, resolved features separated by a few tens of nanometers. This work demonstrates that openFrame-enabled easySTORM instrumentation can be widely accessible to provide affordable, research grade super-resolution microscopy capability for academic and medical research.

Inhibition of diastatic yeasts by Saccharomyces killer toxins to prevent hyperattenuation during brewing.

Secondary fermentation in beer can result in undesirable consequences, such as off-flavors, increased alcohol content, hyperattenuation, gushing, and the spontaneous explosion of packaging. Strains of Saccharomyces cerevisiae var. diastaticus are a major contributor to such spoilage due to their production of extracellular glucoamylase enzyme encoded by the STA1 gene. Saccharomyces yeasts can naturally produce antifungal proteins named "killer" toxins that inhibit the growth of competing yeasts. Challenging diastatic yeasts with killer toxins revealed that 91% of strains are susceptible to the K1 killer toxin produced by S. cerevisiae. Screening of 192 killer yeasts identified novel K2 toxins that could inhibit all K1-resistant diastatic yeasts. Variant K2 killer toxins were more potent than the K1 and K2 toxins, inhibiting 95% of diastatic yeast strains tested. Brewing trials demonstrated that adding killer yeast during a simulated diastatic contamination event could prevent hyperattenuation. Currently, most craft breweries can only safeguard against diastatic yeast contamination by good hygiene and monitoring for the presence of diastatic yeasts. The detection of diastatic yeasts will often lead to the destruction of contaminated products and the aggressive decontamination of brewing facilities. Using killer yeasts in brewing offers an approach to safeguard against product loss and potentially remediate contaminated beer.IMPORTANCEThe rise of craft brewing means that more domestic beer in the marketplace is being produced in facilities lacking the means for pasteurization, which increases the risk of microbial spoilage. The most damaging spoilage yeasts are "diastatic" strains of Saccharomyces cerevisiae that cause increased fermentation (hyperattenuation), resulting in unpalatable flavors such as phenolic off-flavor, as well as over-carbonation that can cause exploding packaging. In the absence of a pasteurizer, there are no methods available that would avert the loss of beer due to contamination by diastatic yeasts. This manuscript has found that diastatic yeasts are sensitive to antifungal proteins named "killer toxins" produced by Saccharomyces yeasts, and in industrial-scale fermentation trials, killer yeasts can remediate diastatic yeast contamination. Using killer toxins to prevent diastatic contamination is a unique and innovative approach that could prevent lost revenue to yeast spoilage and save many breweries the time and cost of purchasing and installing a pasteurizer.

Investigating the potential of a battery swapping method at refuel stations for electric vehicle: A case study of INDIA

Electric vehicles (EVs) are introduced to mitigate environmental problems and develop sustainable modes of transport across the globe. Researchers often report that the non-availability of charging infrastructure is the primary concern for EV adoption. The traditional plug-in EV charging method has a higher waiting time, discouraging users from adopting EVs. Many countries, including India, are promoting an alternative battery swapping method (BSM) for EV charging to reduce the waiting time. But the user preference and willingness to adopt BSM remain unexplored, especially in developing countries. The existing refuel stations are a potential location for developing charging infrastructure that provides the EV charging service and helps create awareness among users because of their wide visibility. Thus, this study aims to bridge the research gap by exploring the influential factors, user preferences, and willingness to use the BSM at refuel stations. For this purpose, 1013 samples were collected from road users who visited 51 refueling stations using the random sampling method in the twin cities of Ahmedabad and Gandhinagar. An integrated partial least squares-structural equation modelling with the artificial neural network method was adopted in this study. The study’s findings reveal that the development of public EV charging facilities at refuel stations significantly impacts the user’s willingness to shift to EVs. The convenience and cost-related motivating factors like a lesser waiting time compared to the plug-in charging method, reduced range anxiety, no concern about battery usage, reduced initial purchase cost of an EV, and comparatively lower maintenance costs significantly motivates user’s willingness to adopt BSM. Similarly, battery-related demotivating factors like the non-reliable range of the swapped battery, non-standard battery design in terms of type, size, capacity, and brand across the country, additional cost for leasing or renting the battery, and chances that EV batteries might be replaced with fake batteries concern BSM adoption.

Optimal low-carbon scheduling of integrated energy systems considering stepped carbon trading and source-load side resources

Under the dual-carbon goal of achieving carbon peaking and carbon neutrality, the Integrated Energy System (IES) enhances the power sector's environmental sustainability by integrating multiple energy sources. To enhance the low-carbon utilization of IES energy, this paper introduces an economic optimization model that incorporates stepwise carbon trading and both source and load-side resources. Initially, the model integrates a Concentrated Solar Power Plant (CSPP) with a Combined Heating and Power (CHP) unit for bidirectional electricity-heat conversion, and incorporates Power to Gas (P2G) and Carbon Capture System (CCS) for carbon recycling within the IES. On the demand side, demand response optimizes the electricity-heat load curve. Subsequently, a stepped carbon trading mechanism guides the system in controlling carbon emissions and constructing a carbon emission model. Finally, the model minimizes the sum of energy purchase costs, carbon transaction costs, demand response compensation costs, wind curtailment penalties, and equipment maintenance costs to optimize the IES economically. By establishing five scenarios for comparative analysis, incorporating CSPP, P2G-CCS, demand response, and a stepped carbon trading mechanism reduces system costs, carbon emissions, and abandonment rates by 21.1 %, 30.87 %, and 23.78 %, respectively, validating the model's effectiveness in promoting a low-carbon economy.

SP1.13 - An Overview of Laparoscopic Surgical Training Modalities – How Does Augmented Reality Simulation Compare?

Abstract Background Laparoscopic surgery is known to require intensive training to gain the required skills and with the increasing popularity of laparoscopic surgery over open surgery requires novel and efficient ways of training. AR is a novel introduction to laparoscopic surgical training and can superimpose digital images and physical models with the unique advantage of combining visual realism with true-to-life haptic feedback. Methods Compare different types of laparoscopic training modalities and assess how AR compares to the education system, 31 papers were systematically reviewed, and the findings were compiled. Results Both the available products as well as the technology as an educational modality were evaluated. Results Studies reported relatively increased speed of learning, improved ability to multitask, procedural accuracy, hand-eye coordination and bimanual operation in a reduced practice time and increased success rate with AR in healthcare education. One of the limitations of AR is that it doesn’t reflect the non-technical skills required in the operation. Compared with other modalities AR provided faster skill acquisition and was widely preferred, except when compared against a human cadaver model for straight laparoscopic colorectal skills acquisition. AR tends to have a higher initial purchasing cost but does not require the costly specialist facilities or services that cadaver or animal models do, 150 trusts in the NHS preliminary data is showing an estimated annual saving of £117m. Conclusion AR simulation has the potential to become the new gold standard for laparoscopic surgical training, and beyond, pending further development of literature to increase knowledge of the technology and its capabilities, on top of technological evolution to increase clinical realism.

Clean energy ship application to promote carbon neutrality in global maritime industry: State governance or international supervision？

The global maritime industry is facing the goal to achieve "carbon neutrality", and substituting traditional ship fuel with clean energy is the most important solution. Whether to promote clean-energy ships through state governance or unified international supervision to achieve carbon neutrality has been a long-standing debate. This study employs both a tripartite evolutionary game model and a quadrilateral evolutionary game model to compare the effectiveness of state governance and unified international supervision in promoting clean-energy ships. The tripartite model simulates interactions between local governments, shipping companies, and shippers, revealing that high subsidy costs reduce local governments' incentives to promote clean-energy ships. The quadrilateral model introduces international supervision, demonstrating that a unified international oversight mechanism can accelerate the global maritime industry's progress toward carbon neutrality. Numerical simulations indicate that lowering the purchase costs of clean-energy ships and introducing government subsidies significantly influence the adoption of clean-energy ships, while supervision costs impact the effectiveness of international regulations. These findings provide critical insights for policymakers aiming to balance financial incentives and global supervision strategies to foster low-carbon development in the maritime sector.

The importance of heat pump cost reduction and domestic supply chain development in the presence of persisting energy price shocks

In this paper we conduct economy-wide scenario simulations to investigate whether and how the wider economic stimulus associated with deploying heat pumps in the UK may help mitigate the macroeconomic and distributional impacts of persistently high electricity and gas prices. Our results show that expansionary processes triggered by producing and installing heat pumps can help offset contractionary pressures associated with higher energy prices. However, outcomes depend on the extent of domestic supply chain content in manufacturing heat pumps, on installation costs to households, on how and where government revenues accrue. Moreover, the relative importance of drivers varies over time. For example, higher purchase and installation costs may support a greater initial stimulus via producer and domestic supply chain gains, despite the greater cost burden on households. However, over time, cost reductions are required to deliver the most favourable economy-wide outcomes.

When Should a Sharing Platform Adopt the Bilateral Review System?

The increasing popularity of sharing platforms is raising concerns about a lack of information on the demand side: the cost of serving specific buyers is typically unknown to sellers. To mitigate this concern, some platforms, such as Airbnb, have adopted the bilateral review system (BRS), allowing buyers and sellers to rate each other. This differs from the traditional unilateral review system (URS), which allows only buyers to rate sellers. In this study, we examine how these two review systems impact the operation of a peer-to-peer (P2P) sharing platform, where sellers with different qualities are matched with buyers with different serving costs. Our analysis reveals that under URS, even with perfect seller information, high-quality sellers can still be driven out of the market due to unknown information about the buyer and the “coproduction” nature of the serving cost. This differs from the adverse selection problem often observed in the used car market, for example, where sellers of high-quality products withdraw due to generally unknown product quality. Additionally, we highlight the critical role of the expected buyer cost: When this cost is high, BRS can benefit the platform by alleviating the adverse selection problem; however, when this cost is low, BRS becomes detrimental to the platform. Our analysis also shows that BRS helps buyers but can hurt sellers. However, BRS has the potential to generate a favorable outcome for all parties involved, resulting in a win-win-win situation benefiting the platform, buyers, and sellers. Our results not only shed light on review system design but also provide a plausible explanation for the widespread use of BRS by sharing platforms such as Airbnb, Fiverr, and Turo.

МОДЕЛЬ ОПТИМІЗАЦІЇ ФУНКЦІОНУВАННЯ МІКРОМЕРЕЖІ З СЕС ТА УСТАНОВКОЮ ЗБЕРІГАННЯ ЕНЕРГІЇ

The article provides a description of the microgrid function optimization model for solving the problem of reducing the cost of electricity purchases by the owner of such a microgrid on the retail electricity market. The objective function of minimizing the cost of electricity purchase has been formalized. Approaches to determining the coefficients of penalty functions designed to cut off technically unacceptable modes of the microgrid have been determined. A mathematical model of an energy storage system (ESS) is proposed, in which the functions of calculating the technological costs of electricity are formalized. A system of constraints for the problem of minimization of the cost of electricity purchase is given. Approaches to solving the problem of evaluating the effect of SPP and ESS operation in microgrids using optimization results are determined. Examples of microgrid operation optimization for an enterprise with its own SPP and ESS are presented. The need for additional control of the economic expediency of involving ESS in the regulation of electricity balances at the enterprise was noted. References 17, figures 3, table 1.

Quantitative recovery and regeneration of basic deep eutectic solvent for biomass treatment with industrialized membrane-based strategy

As a new type of ionic liquids, basic deep eutectic solvents have been reported as emerging medium for biorefinery, extraction, catalysis and so on. Quantitative recovery with recycling of basic DES was important for the scale utilization of biomass and the environmental conservation. An electrodialysis-based method was designed for divisionally regenerating basic DES choline chloride-monoethanol amine (ChCl-MEA) after pretreatment of wheat straw. Influence of primary process parameters on recovery of ChCl-MEA was studied and recovery economy of ChCl-MEA was analyzed. Recovery ratio of ChCl and MEA varied within 89.2 %-96.0 % and 93.3 %-97.9 %. Energy consumption of ChCl-MEA recovery changed within 4.2–29.4 kW·h/kg. Transport rate of basic DES ChCl-MEA altered in the range of 12.2–464.7 mmol/(m2·min). Process economy analysis proved that recovery cost of basic DES ChCl-MEA was less than 7 % (0.81–7.07 $/kg) of DES purchase cost. Insight brought by this study indicated a technically feasible and economical approach for basic DES recycling after biomass pretreatment.

A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

Integrated planning for a multiproduct, multisite reforestation value chain: a Canadian case study

This paper addresses integrated supply and distribution planning for a multiproduct, multisite reforestation value chain (RVC) in a make-to-order context. The aim is to develop a tactical planning tool to determine the quantities of seedling species and sizes to order from nurseries to satisfy reforestation site needs while minimizing purchase and distribution costs. A mixed-integer linear programming model was developed and applied to the publicly managed RVC in the province of Quebec, Canada. This tool was also used to analyze scenarios considering the three pillars of sustainability: environmental (climate risk management to bolster resilience), social (fair production allocation), and economic (supply and distribution costs) aspects. This strategic analysis highlights the trade-offs between purely economic and more sustainable scenarios and discusses strategic deployment opportunities to improve the RVC. Ultimately, it serves as a powerful tool for efficiently allocating yearly seedling supply, minimizing costs, and promoting sustainable practices in reforestation planning which provides valuable insights for RVC managers.

Perencanaan Sumber Daya Material pada Pekerjaan Arsitektur dengan Material Requerement Planning (MRP) pada Proyek Gedung Bertingkat

Construction in Indonesia is one of the government's top priorities in an effort to improve the welfare of the society. Construction planning must be carefully planned in order to achieve efficiency in project implementation, in terms of time and cost. A construction project requires planning and controlling material inventory. The purpose of material planning and control is so that construction projects can run smoothly, effectively and efficiently. Material planning and control can be analyzed using the material requirements planning (MRP) method. In this study, using economic order quantity (EOQ) and period order quantity (POQ) techniques. Cost budget design data, s-curves, work unit price analysis, and work progress reports are the data components to be processed. The results showed the cost of procurement of materials needed with the EOQ technique, namely: purchase cost of IDR 8,466,623,718.49, ordering cost of IDR 1,598,050.00, and storage cost of IDR 8,942,979.62. The total cost required is IDR 8,477,164,748.11. Furthermore, for the POQ technique, namely: purchase cost of IDR 8,466,623,718.49, ordering cost of IDR 1,598,050.00 and storage cost of IDR 9,674,554.05. The total cost required is IDR 8,477,896,322.53 Comparison of the costs required from the EOQ and POQ techniques can be concluded that Material Requirement Planning (MRP) using the Economic Order Quality (EOQ) technique is less costly than the Period Order Quantity (POQ) technique. Material procurement using Economic Order Quality (EOQ) is more effective. This is because the EOQ technique requires less costly / economic procurement compared to the POQ technique. In addition, with the application of the EOQ technique, there is a clearer material ordering schedule and the amount of material ordered is in accordance with the needs so that the material storage is not too much.

Enhancing supply chain relationships in the circular economy: Strategies for a green centralized supply chain with deteriorating products

This article introduces a green centralized supply chain in a two-stage stochastic programming model using deteriorating products. The model reduces the cost of purchasing, transporting, storing, product recovery and shortages. This cuts down on greenhouse emission related to transportation, product recovery, and recycling programs. On the basis of this, we explore the utilization of the circular economy to the damages that could occur from used products. Furthermore, revenue sharing and quantity discount contracts are examined in the business models between the members of the supply chain and the external manufacturer. Demand is assumed to be uncertain, and scenarios are created to account this. The model specifies the optimal order quantities, transportation modes and contract terms that minimize costs and environmental impacts. Numerical examples analyze the trade-offs between economic and environmental objectives under different supply chain parameters. The results provide insights for circular supply chains that reconcile economic incentives with environmental responsibility for deteriorating product.

Value for money of reusable versus disposable ophthalmic instruments for intravitreal injections

aim: The aim of this study was to follow the instruments’ pathways and cost each segment to calculate whether reusable or disposable ophthalmic instruments offer better value for money for intravitreal injections. methods: The cycles and costs of reusable and single-use disposable instruments used for intravitreal injections were mapped out, including purchase costs, transport to and from the place of use, opening and disposal, sterilisation, replacement, salary costs of staff involved, etc. results: The cost of using reusable instruments for intravitreal injections (NZ$29.00) was lower than the cost of using disposable instruments ($30.51) by $1.51 per patient. conclusions: Intravitreal injections performed with reusable instruments offer better value for money than when performed with disposable instruments. This equates to a beneficial financial saving just for this one low-complexity case. Such savings can multiply significantly when considering the instruments used in a wider variety of ophthalmic procedures. There are of course trade-offs between safety, quality, cost and sustainability.

Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation

Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency.

Two-Warehouse Inventory Model: Interval Valued Costs, Advanced Payment, Stock-Dependent Demand, and Partial Backlogging

A great deal of inventory costs are typically unpredictable because of the unpredictability of a competitive market. The fluctating demand from customer's and the unpredictability of the market economy make it difficult for researchers and operation research practitioners to appropriately replicate an inventory problem. In order to prevail this type of unpredictability circumstances, in this paper, we have represented the inventory parameters as interval. Using this concept, we progressed a two-warehouse inventory model for deteriorating items with a fixed Shelf life, partially backlogging shortages and interval-valued deterioration rate. In addition to this the parameters like ordering cost, purchase cost, shortage cost, deterioration cost for both the rented and owned warehouse except the backlogging parameter have been considered as interval-valued. The uncertainty in the inventory parameters motivated us to view them as interval-valued in the present research. Based on the assumptions, the cost function of this problem is a highly nonlinear constraint optimization problem. Mathematica is used to tackle this nonlinear optimization problem. A numerical example has been presented to demonstrate the computational result. Then, a sensitivity analysis has been performed to study the effect of changes of different parameters of the model on the optimal policy.

Preparation of oxygen-sensitive proteins for high-resolution cryoEM structure determination using (an)aerobic blot-free vitrification.

High-quality grid preparation for single-particle cryogenic electron microscopy (cryoEM) remains a bottleneck for routinely obtaining high-resolution structures. The issues that arise from traditional grid preparation workflows are particularly exacerbated for oxygen-sensitive proteins, including metalloproteins, whereby oxygen-induced damage and alteration of oxidation states can result in protein inactivation, denaturation, and/or aggregation. Indeed, 99% of the current structures in the EMBD were prepared aerobically and limited successes for anaerobic cryoEM grid preparation exist. Current practices for anaerobic grid preparation involve a vitrification device located in an anoxic chamber, which presents significant challenges including temperature and humidity control, optimization of freezing conditions, costs for purchase and operation, as well as accessibility. Here, we present a streamlined approach that allows for the (an)aerobic vitrification of oxygen-sensitive proteins using an automated aerobic blot-free grid vitrification device - the SPT Labtech chameleon. This robust workflow allows for high-resolution structure determination of dynamic, oxygen-sensitive proteins, of varying complexity and molecular weight.

Synergizing renewable energy sources in building-integrated hybrid energy systems via niche-ant colony optimization

The rapid advancement of Building-Integrated Hybrid Energy Systems (BIHES), characterized by renewable energy sources with variable generation patterns, presents significant challenges for effective integration and utilization. This paper introduces an optimal economic dispatch strategy for commercial BIHES using a Niche-Ant Colony Optimization (NACO) Algorithm. BIHES integrates renewable sources such as 200 kW photovoltaic panels, 150 kW wind turbines, and 500 kWh energy storage systems within commercial buildings. A detailed mathematical model for economic dispatch leverages a multi-load demand of 1.2 MW and flexible adjustment capabilities. The model aims to maximize on-site renewable energy use while balancing grid interaction. The NACO Algorithm, tested through comprehensive simulations, demonstrated significant improvements, resulting in a 12.96 % reduction in environmental costs, an 18.4 % reduction in the Levelized Cost of Energy (LCOE), and a 14.25 % reduction in total operational costs compared to traditional methods. Additionally, the system achieved annual CO2 savings of 25,000 tons, increased renewable energy absorption by 22 %, reduced grid dependency by 30 %, and decreased energy purchase costs by 20 %. Energy storage optimization also extended the storage system's lifecycle by 15 %. These results highlight the enhanced synergy and complementarity of multiple energy sources within BIHES, showcasing its potential for substantial economic and environmental benefits.

Optimizing environmental costs and economic benefits in new energy vehicle production: A case study of FAW Hongqi in China

This study focuses on the environmental cost accounting and economic benefit optimization of China’s FAW Hongqi New Energy Vehicle manufacturing enterprise under uncertain conditions, within the context of the emission permit system This study calculates the pollution situation throughout the manufacturing and production process of FAW Hongqi new energy vehicles, and constructs a multi-level environmental cost evaluation system for FAW Hongqi new energy vehicle manufacturing projects. Through the interval fuzzy model of FAW Hongqi new energy vehicle manufacturing projects, the maximum economic benefits of the enterprise are simulated. The research results indicate that the pollution emissions of enterprises are mainly concentrated in the three processes of welding, painting, and final assembly. Enterprises use their own exhaust gas and wastewater treatment devices to meet the standards for pollution emissions. At the same time, solid waste generated during the automobile manufacturing process is handed over to third-party companies for treatment. Secondly, based on the accounting results of enterprise pollution source intensity and a multi-layer environmental cost evaluation system, the environmental costs of enterprises are accounted for, and the environmental costs are represented in interval form to reduce uncertainty in the accounting process. According to the accounting results of enterprise environmental costs, the main environmental costs of enterprises are environmental remediation costs caused by normal pollution discharge and purchase costs of environmental protection facilities. Pollutant emission taxes and routine environmental monitoring costs are relatively low. Enterprises can adopt more scientific solutions from the aspects of environmental remediation and environmental protection facilities to reduce environmental costs. After optimization by the fuzzy interval uncertainty optimization model, the economic benefits of the FAW Hongqi new energy vehicle manufacturing project were [101,254.71, 6278.5413] million yuan. Compared with the interval uncertainty optimization model, the lower bound of economic benefits increased by 57.68%, and the upper bound decreased by 12.08%, shortening the results of the economic benefits interval. Clarify the current environmental pollution situation of FAW Hongqi’s new energy vehicle manufacturing enterprise, provide data support for sustainable development of the enterprise, and provide reasonable decision-making space for enterprise decision-makers.