Pipeline Utilization Research Articles

Pipeline sharing: Optimal design of downstream green ammonia supply systems integrating with multi-product pipelines

Green ammonia is a promising energy storage form with extremely high efficiency of hydrogen storage. Although the construction of green ammonia production facilities is rapidly progressing, there is still a lack of comprehensive consideration of the optimal design for supply systems. This paper prospectively explores a framework for the ammonia supply system under four scenarios considering multimodal transportation. Different mathematical models are developed, which incorporate special constraints such as pipe diameter, broken loops, etc., aiming at minimizing costs of transportation, construction and carbon treatment. In this framework, the economic, energy and environmental performance is evaluated to optimize the design of the supply system. The case study in China shows that different transportation modes can reduce economic costs by 60.93∼77.45 %, energy consumption by 18.4 ∼67.7 ktce, and carbon emissions by 23.3∼60.7 kt per year. A further novel finding is that the simultaneous usage of new liquid ammonia pipelines and multi-product pipelines for supply systems proves to be the most effective transportation mode. Sensitivity analyses are also conducted on regional production capacity expansion, liquid ammonia pipeline planning, and multi-product pipeline utilization. The results confirm that the scheme is stable under certain conditions. Finally, some recommendations are provided for the development of green ammonia industry.

OP135 Machine Learning And Cancer Registry: Evaluation Of The Effectiveness Of Case Coding

IntroductionMachine learning (ML) algorithms are computational procedures that use pattern recognition and inference by learning from previously categorized documents to predict the category to which a new document belongs. The role of machine learning within cancer registries remains unclear given the lack of in-depth testing and guidance from health technology assessment (HTA) agencies. We evaluated the effectiveness of coding new cases through machine learning at the Integrated Cancer Registry.MethodsThe Integrated Cancer Registry covers the eastern area of Sicily in Italy, which has an annual average incidence of about 10,000 cases of malignant neoplasm. Potential new cancer cases were retrieved from pathology services and processed by pathologists who confirmed the neoplastic nature of supposed cases and specified the morphological type and location of the tumors. The current method involves identification by reading the free-text report when International Classification Diseases for Oncology information was not provided. We used the new Microsoft ML.Net Library, a framework developed in response to the challenge of facilitating machine learning pipeline utilization in large software applications. A total of 1,050,952 free-text pathology reports published from 2003 to 2018 were selected separately from all Sicilian pathology services and uploaded to machine learning software that explored eight binary classification algorithms.ResultsWe evaluated each algorithm’s performance by calculating metrics (the number of true positives, true negatives, false positives, and false negatives) from the classification procedure applied to the test dataset. The metrics used were accuracy, F1 score, and area under the receiver operating characteristic curve. With a test set of around 210,000 text diagnoses, each algorithm reached an F1 score of up to 95 percent.ConclusionsMachine learning algorithms capture relevant information about tumors from free-text pathology reports, optimizing the process and reducing waste. With the help of machine learning systems, cancer registries can provide more timely data for research and evaluation of all types of new cancer technologies (drugs, devices, radiology and radiotherapy equipment, diagnostic devices, robotic surgery, and vaccines).

A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health.

The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health. To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them. SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% for geographic coordinates. SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors.

Development and utilization of phenotyping pipelines to assess resilience to transient abiotic stress in vegetable crop populations

Development and utilization of phenotyping pipelines to assess resilience to transient abiotic stress in vegetable crop populations

How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design

AbstractExtra‐organismal DNA (eoDNA) from material left behind by organisms (noninvasive DNA, e.g., feces, hair) or from environmental samples (eDNA, e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost‐ and time‐intensive replications of genotyping/sequencing procedures. Therefore, system‐ and site‐specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using 10 environmentally diverse bat roosts as a case study, we developed a robust modeling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling‐site‐specific ideal exposure duration. Maximum humidity was the strongest eoDNA‐degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site‐specific simulations revealed that reducing exposure duration to 2–8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA‐based species detection, and increasing the accuracy of abundance estimates.

Optimizing automated white matter hyperintensity segmentation in individuals with stroke.

White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.

Assessing benefits in the flexibility of refined oil logistics from pipeline network integration reform: A case from South China

• Propose a framework for flexibility quantification of oil logistics flexibility. • The framework based on MILP model contains both quantitative and qualitative parts. • Build a calculation method for the flexibility indicator of refined oil logistics. • Quantify the impact of the reform on logistics flexibility in South China. The pipeline network integration reform enables unified management of pipelines from different entities. For refined oil logistics, this paper proposes a framework based on the MILP optimization model to quantify its flexibility. Considering the uncertainty, three disturbances occur in the logistics concurrently, and 10,000 simulations are performed to obtain the turnover cost. The ratio of pipeline transportation cost to the calculated average turnover cost is defined as the flexibility indicator. Taking China's largest refined oil pipeline network as an example, the results show that the flexibility rises 8.9% after the reform. The paper also quantifies the impact of the reform on logistics flexibility in South China, which is embodied in achieving lower freights and GHG emissions, lower impact by fluctuations, higher pipeline utilization, more efficient oil product turnover, and the avoiding of depot shortages when facing logistical disturbances. The underlying reasons for the results and 3E analysis are analyzed.

Efficient Hint-Based Event (EHE) Issue Scheduling for Hardware Multithreaded RISC-V Pipeline

Hardware multithreading is a common approach for tolerating memory latency by utilizing idle cycles and avoiding CPU stalling. Nowadays, multithreading architectures are commonly used across many processors and various embedded edge devices to improve performance. This work suggests a new multithreading in-order pipeline microarchitecture design for RISC-V and proposes an efficient event-based issue scheduling algorithm. The proposed scheduling algorithm is based on the unique RISC-V ISA that enables decoding of the instruction type in an early stage of the pipeline. The RISC-V-based multithreading architecture is evaluated using a dedicated software simulator. Simulation results show that the proposed algorithm outperforms the classical Round Robin and the coarse grain algorithms. The proposed architecture is evaluated using the standard MiBench benchmark and other common applications, demonstrating pipeline utilization improvement of up to about 26% in terms of IPC using four threads.

Continuous time and volume batch formulation for the multiproduct pipeline network scheduling problem

The pipeline is one of the most reliable and economical modes of transport. It is mainly used for petroleum products transportation over long distance. In this paper, we are interested in the multiproduct pipeline network scheduling problem in the context of mining industry. The considered pipeline network is part of OCP's pipeline network for the transportation of phosphate products. A MILP model based on continuous time and volume batch formulation is proposed. It aims to schedule a set of batches to be transported from the main pipeline to intermediate storage groups before delivered to Clients using secondary pipelines. Each batch can be composed of several blocks. The objective is to maximize the main pipeline utilization rate with smallest number of blocks to satisfy all clients’ demands. A set of short-term plan horizon instances are generated and the results show that the proposed model gives effectively optimal or near-optimal solutions of these instances.

OPTIMIZATION OF GAS PIPELINE UTILIZATION FOR SECTION 2 PEMPING-TANJUNG UNCANG WITH THE PROVISION OF MINI LNG PLANT FOR KARIMUN REGENCY

Investment evaluation is a crucial part of investment decisions to measure will the project generate profit for the company. There is four Capital Budgeting technique used to measure this investment evaluation in this project Net Present Value (NPV), Internal Rate Return (IRR), Discounted Probability Index (DPI), and Payback Period (PBP). From the evaluation, it was obtained that a positive NPV of 280.649, an IRR of 8,10% greater than the WACC of 4,21%, while the DPI of 1.25 and PBP of 3,25 years was faster than the duration of the 5 (five) year contract. Monte Carlo simulation used 1.000 times to calculate Probability NPV<0 with result Probability NPV<0 in this project is 0,17% meanwhile probability NPV>0 is 99,83%.In the sensitivity analysis, it is found that the increase in the cost of capital and the duration of the agreement are factors that are sensitive to project feasibility. From the results of the above calculations, it can be concluded that Optimization Of Gas Pipeline Utilization For Section 2 Pemping – Tanjung Uncang With The Provision Of Mini LNG Plant For Karimun Regency is Eligible to be accepted.

Impacts of energy transition on Liquefied Natural Gas shipping: A case study of China and its strategies

Energy transition has been a global trend for decades, but very little literature can be found on how energy shipping is affected. Particularly, the coal-to-gas transition of China has intensified the nation's reliance on Liquefied Natural Gas (LNG) shipping import, especially through a strategic maritime link, the Straits of Malacca and Singapore (SOMS). If the SOMS is blocked in conflicts or incidents, energy supplies of China will be disrupted. Driven by practical significance and the literature gap, a case study of China is formulated to investigate how shocks in natural gas supply and demand will impact LNG shipping import volumes of China and its reliance on the SOMS, especially in the context of energy transition and the Belt and Road Initiative (BRI). An innovative System Dynamics model is developed to simulate natural gas supply and demand of China based on historical data, policies, and market information (i.e. energy prices, BRI projects and LNG contracts). It is recommended for China to create financial incentives to support renewable energy and shale gas development, enhance natural gas pipeline utilization and encourage overseas LNG infrastructure investments. BRI members are recommended to diversify export routes, expanding shares in global pipeline natural gas and LNG markets.

Aneuploidy: An opportunity within single-cell RNA sequencing analysis

Single-cell sequencing data has transformed the understanding of biological heterogeneity. While many flavors of single-cell sequencing have been developed, single-cell RNA sequencing (scRNA-seq) is currently the most prolific form in published literature. Bioinformatic analysis of differential biology within the population of cells studied relies on inferences and grouping of cells due to the spotty nature of data within individual cell scRNA-seq gene counts. One biologically relevant variable is readily inferred from scRNA-seq gene count tables regardless of individual gene representation within single cells: aneuploidy. Since hundreds of genes are present on chromosome arms, high-quality inferences of aneuploidy can be made from scRNA-seq datasets. This viewpoint summarizes how utilization of these bioinformatic pipelines can benefit scRNA-seq studies, particularly in oncology wherein aneuploidy is both rampant and a hallmark of the studied disease. Awareness and use of these analytical pipelines will improve each field’s ability to understand the studied diseases. Authors are encouraged to attempt these aneuploid analyses when reporting scRNA-seq data, much like copy-number variants are commonly reported in bulk genome sequencing data.

Research on explosion suppression technology of low-concentration gas transmission pipeline

In order to effectively control the explosion accident disasters of low-concentration gas transmission pipelines and improve the safety of low-concentration gas transmission and utilization process, the paper has summarized and analyzed the current three types of low-concentration gas transmission pipeline explosion suppression technologies and devices used in the field, including powder suppression, inert gas suppression and fine water mist suppression, and put forward the problems of coal mine gas transmission pipeline explosion suppression technologies. It is of great practical significance to pipeline transmission and utilization of low-concentration gas.

Feasibility of collection and analysis of microbiome data in a longitudinal randomized trial of community gardening.

Aim: We explored the feasibility of collecting and analyzing human microbiome data in a longitudinal randomized controlled trial of community gardening. Methods & materials: Participants were randomly assigned to gardening (N= 8) or control (N = 8). Participants provided stool, mouth, hand and forehead microbiome samples at six timepoints. Analyses combined mixed models with Qiita output. Results: Participant satisfaction was high, with 75% of participants completing evaluations. While no microbial effects were statistically significant due to small sample size, the analysis pipeline utility was tested. Conclusion: Longitudinal collection and analysis of microbiome data in a community gardening randomized controlled trial is feasible. The analysis pipeline will be useful in larger studies for assessment of the pathway between microbiota, gardening and health outcomes.

A Power-Efficient CNN Accelerator With Similar Feature Skipping for Face Recognition in Mobile Devices

A low power face recognition (FR) convolutional neural network (CNN) processor is proposed with high power efficiency to achieve always-on FR in mobile devices. Three key features enable a power-efficient FR CNN. First, tile-based clustering (THC) is proposed for reducing the computation overhead of hierarchical clustering. It generates an average of 37.2% duplicated input features in the entire network. Second, a low latency tile-based hierarchical clustering core is proposed. It supports an approximated clustering method that removes distance updates and increases pipeline utilization by up to 98.6%. Finally, a similar feature skipping binary convolution core with separated accumulation cores is proposed to increase power efficiency through skipping redundant MAC operation between duplicated input features and weights. It can skip 38.9% of CNN operations and reduce 35.8% of the total computation considering both CNN and THC. The proposed features enable the proposed processor to operate FR CNN with 17.3 TOPS/W power efficiency, which is $1.3\times $ higher compared to the previous state-of-the-art FR CNN processor. Implemented in 65nm CMOS technology, the 6mm2 FR CNN processor shows 0.5mW power consumption at 1 frames-per-second always-on face recognition.

Future scenario of China's downstream oil reform: Improving the energy-environmental efficiency of the pipeline networks through interconnectivity

Improving energy supply efficiency and quality is regarded as a key pathway to shifting towards a fully sustainable energy system. To address the low efficiency and high emissions in the downstream oil industry caused by the problem of vertically integrated monopoly, the Chinese government is making an effort to promote a multiproduct pipeline network reform. The fundamental and indispensable step for this goal is the pipeline network interconnectivity. This paper quantifies the energy-environmental impacts of the pipeline network interconnectivity reform on China's downstream oil supply chain to 2030. An integrated framework is developed to obtain the detailed design-scale information required for assessment, introducing demand forecasting and demand reallocation into a pipeline network optimal planning model. The model is formulated as a fuzzy mixed integer linear programming that optimizes the infrastructure development scheme and supply chain operation plan simultaneously while taking into account demand uncertainty. The results show that, compared with the baseline, the pipeline interconnectivity reform could reduce yearly energy consumption and CO2 emissions by 9.7–19.8% and 12.5–17.9%, respectively. It is shown that this reform policy could overcome infrastructure constraints, increase pipeline utilization, and improve both energy and environmental performance. The proposed framework can be a theoretical guideline for policymakers within and beyond China.

A Multi-Task Hardwired Accelerator for Face Detection and Alignment

Face detection and alignment are two fundamental tasks for facial applications and the corresponding accelerators have been designed to enable energy-efficient acceleration. However, these dedicated accelerators are always designed separately, thereby ignoring the inherent correlation between face detection and alignment and causing additional communication and area overhead. Based on this motivation, a multi-task cascaded convolutional networks (MTCNN) algorithm-based accelerator is presented in this work to support both face detection and alignment for multiple faces. First, multiply-accumulate (MAC) operations and memory access of the magnification process in the resize module are reduced by 22.8&#x0025; and 24.8&#x0025; on average, respectively, when compared with those of similar methods. Second, clustering non-maximum suppression (C-NMS) is proposed to significantly reduce the intersection over union computation and eliminate the hardware-inference sorting process in NMS, yielding a 16.0&#x0025; speedup in the overall process. Third, an efficient pipeline architecture is proposed to implement a complexity- and memory-intensive proposal network of MTCNN in a more computationally efficient manner, with 38.3&#x0025; less memory capacity than a similar solution. Meanwhile, only approximately half of the multipliers are needed to achieve the same throughput with high pipeline utilization. Fourth, considering the variable number of faces in each input, a batch schedule mechanism is proposed to improve the fully-connected layer hardware utilization by 16.7&#x0025; on average in the batch process. Based on a simulation with the TSMC 28 nm CMOS process, this accelerator consumes only 10.9ms at 400 MHz to simultaneously process 5 faces. The power efficiency reaches 4.80 TOPS/W, which is <inline-formula> <tex-math notation="LaTeX">$227.4\times $ </tex-math></inline-formula> higher than that of the state-of-the-art solution.

Set of indicators for dependability evaluation of gas compression units

The paper is dedicated to the improvement of the evaluation methods of one of the most important operating characteristics of gas compression units (GCUs), i.e. dependability, under the conditions of decreasing pipeline utilization rate. Currently, the dependability of units is characterized by a set of parameters based on the identification of the time spent by a unit in certain operational state. The paper presents the primary findings regarding the dependability coefficients of GPA-Ts-18 units, 41 of which are operated in multi-yard compressor stations (CSs) of one of Gazprom’s subsidiaries. The dependability indicators (technical state coefficient, availability coefficient, operational availability coefficient) identified as part of the research are given as well. GCUs were classified into groups depending on the coefficient values. The feasibility of using integral indicators in the analysis of GCU groups’ dependability was examined. It was proposed to use confidence intervals for identification of the integral level of dependability of the operated GCU stock and the ways of maintaining the operability of units under the conditions of decreasing main gas pipeline utilization rate. The Gini index was suggested for the purpose of generalized estimation of GCU groups’ dependability. It is shown that the advantage of the Gini coefficient is that is allows taking into account the ranks of the analyzed features in groups. The graphic interpretation of the findings was executed with a Lorenz curve. The paper implements the sigma rule that characterizes the probability of the actual coefficient value being within the confidence interval, i.e. prediction limits (upper and lower), within which the actual values will fall with a given probability. The confidence intervals were identified by the type of coefficients distribution and a standard deviation, ć. A histogram of an interval range of technical utilization coefficient distribution is given as an example. Testing of the hypothesis of the distribution type at confidence level 0.95 showed that the distribution of coefficients is normal. Using the moment method, the mathematical expectation and mean square deviation for the distribution of the values of each type of dependability indicators were established. Using the sigma rule, all extreme outliers among the GCUs in terms of the level of factor attribute were excluded from the body of input data. All units whose factor attribute value does not fall in the interval were excluded. According to the three sigma rule, 3 and 2 GCUs did not fall in the confidence interval (µ±3σ) in terms of the utilization factor and availability factor respectively. The performed analysis of causes of low availability coefficients of the above GCUs showed that the systems had been long in maintenance. The paper sets forth summary data on the maximum allowable value of the Gini index of dependability coefficients (CTU, CA, COA) depending on the sample size (the complete sample of 41 units and samples with the interval of 1, 2, 3 sigma). In case of higher values of Gini index it is recommended to adopt measures to individual units in order to improve the dependability of the operated GCU stock.

Carbon Dioxide Transport for Geological Sequestration - The Issues and Decision Parameters for T&amp;amp;T

Based on an increasing emphasis on Carbon Dioxide (CO2) sequestration as a carbon management strategy coupled with the growing acceptance that geological sequestration in particular is perhaps one of the most promising carbon management techniques, the inherent aspect of this technique i.e. transportation of CO2 from sources to sinks in a cost effective and safe manner is gaining widespread attention. Emphasis of this mitigation strategy is justified as CO2 transport is often one of the more underestimated components of any geological sequestration capture, transportation and storage chain. Studies were done in evaluating Carbon Capture, Utilisation and Storage (CCUS) economics for Trinidad and Tobago (T&T), however these did not place any great emphasis on critical aspects of CO2 transportation cost estimation through widespread informed estimates and proper transportation modes and infrastructure options. This paper attempts to address this gap for (T&T). The transported CO2 is normally processed in liquid or supercritical phases. The supercritical phase is used with pipelines in continuous flux and the liquid type mainly for batch transportation (e.g. train, truck, ship). CO2 can also be transported in a solid state however this process requires much more energy compared to the other options and is therefore inferior from a cost and energy requirement viewpoint. Transport of CO2 is already a reality, occurring daily in many parts of the world. However, the scale of the transportation infrastructure and investment required to enable large-scale deployment of CCS cannot be underestimated. Since data in these areas is limited for T&T, one of the objectives of this paper is to compare different ways of CO2 transportation from the selected source to the targeted fields in T&T. This analysis will allow for a level of source-sink matching to be incorporated. Among all the options for CO2 transportation, the most economic method of transport depends on factors such as the locations of capture and storage, distance from source to sink, and the quantities of CO2 to be transported. In the case of large amounts of CO2 are to be transported, as may be in the case of T&T, the utilization of pipelines and ships may be the only viable transportation alternatives for CCS purposes. For T&T, the threshold volumes beyond which batch transportation will no longer feasible when compared with the use of pipelines is unknown and the determination of this key threshold value is expected to be another major output of this study. The initial methodology employed in this study involved conducting a comprehensive literature review in the area of CO2 transportation. The sources of data for this review were not limited to T&T alone however, where applicable, local data was incorporated to integrate certain key indigenous parameters (such as relatively cheaper electricity rates) that would significantly influence the transportation cost. Once the data was acquired and contextualized for T&T, a financial operational model was built using Excel software to compute the costs for the various transportation modes. In order to account for fluctuations of parameters associated with the various modes of transport, the models were built incorporating sensitivity analyses to simulate these variations. Since no transport simulations were done in this study, the robustness of data was imperative for the results to be meaningful. The results of this study presents enormous benefits for T&T as this study not only measured but illustrated a functional methodology of the costs associated for transporting up to 6 Mt/yr of CO2 for the different modes while taking into account the effect of internal variations. It is expected that this information will be critical in evaluating overall CCS economics for T&T. It was found that: Transporting 6 MtCO2/y over 71.5km for 10 years would have an estimated capital cost of USD 61.7 million and an OM Considering inflation, the unit cost ranged between 0.62-1.09 US$/tCO2 using the variable pipeline lengths of 40-71.5 km; and The length of the pipeline was shown to have the greatest effect on the transport cost.

Fiscal and Economic Impact Analysis of Proposed Nexus Natural Gas Pipeline on the City of Green, Ohio: A Case Study

In order to provide a sustainable outlet for natural gas harvested from the fracking fields of eastern Ohio, in 2015 Nexus announced plans to build 250 miles of high-pressure natural gas transmission pipeline that would run from the source into Michigan, ultimately to Ontario, Canada. The pipeline route passes through the fast-growing City of Green, Ohio, and could disrupt development plans there. The proposed path would cause the City of Green to disproportionately bear the burden of anticipated economic losses and reduction in tax revenue associated with the pipeline. Despite substantial front-loaded ad valorem taxes paid by the pipeline utility, over a 50-year period, the pipeline is projected to cause NPV fiscal losses of over $52 million, largely from foregone property and income taxes for future development.