Multiproduct Pipeline Research Articles

Complete N and P removal from hydroponic greenhouse wastewater by Tetradesmus obliquus: A strategy for algal bioremediation and cultivation in Nordic countries

Agricultural wastewaters are a major source of pollution in nature. Here, agricultural wastewaters from a commercial hydroponic greenhouse were used to cultivate the microalga Tetradesmus obliquus sp. NIVA-CHL107 in order to assess its bioremediation ability and evaluate the potential held in biomass derived from wastewater. The batch cultivation trials were performed using a pilot scale indoor photobioreactor equipped with LEDs. The culture achieved 100 % removal efficiency of N and P and a maximum dry weight (DW) of 6.2 g L−1. At the exponential phase of growth, the biomass had a protein content of 45–50 % of DW and a carbohydrate fraction representing 20 % of DW. At the stationary phase, the carbohydrates increased to 60 % of DW. The PUFA content was higher during the exponential phase, representing >65 % of total fatty acids. Throughout the cultivation, the predominant carotenoids were lutein and β-carotene. The Tetradesmus obliquus sp. NIVA-CHL107 was demonstrated to be a suitable candidate for an algal biorefinery designed for hydroponic wastewater treatment and a multi-product pipeline.

Locating block caused by stuck PIGs in multi product pipeline using combination of radioisotope techniques

Blockages in pipelines inhibit the normal use of the pipeline due to constricted flow of fluid through the pipeline. Locating blockages in pipelines is a challenge and there is an increasing demand for sensitive inspection methods to locate blockages in pipelines, caused either due to presence of foreign undesired material during commissioning or maintenance of the pipeline or due to accumulation of product remanent during the prolonged use of the pipelines. A block was caused in a multiproduct pipeline due to a stuck pig (pipe inspection gauge). Radiotracer techniques viz. gamma scanning and radiography were employed to pinpoint the block location.

Study on downloading contaminated product at an intermediate station in Lanzhou-Chengdu-Chongqing multi-product pipeline

Study on downloading contaminated product at an intermediate station in Lanzhou-Chengdu-Chongqing multi-product pipeline

Study on downloading contaminated product at an intermediate station in Lanzhou-Chengdu-Chongqing multi-product pipeline

Study on downloading contaminated product at an intermediate station in Lanzhou-Chengdu-Chongqing multi-product pipeline

Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines

To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min

Physics-informed Student’s t mixture regression model applied to predict mixed oil length

Real-time estimation of thelength of mixed oil in a multi-product pipeline is a critical task during batch transportation. In previous studies, various predictive models have been built while they merely depended on a single predictive model to fulfill the regression work, and model performance severely deteriorated with the presence of outliers. The Student’s t mixture regression (SMR) model can identify multimode characteristics and reduce the impact of outliers. However, ignorance of physics knowledge and the simplistic assumption of a linear relationship between variables in the SMR may lead to unsatisfactory performance. In addition, the possible singularity problem can make the SMR fails to work. Motivated by resolving these issues, this paper proposes a physics-informed SMR modeling method by integrating the physics knowledge and the SMR to develop a robust hybrid predictive model for predicting the mixed oil length in a multi-product pipeline. Case studies are carried out on the measured dataset to demonstrate the effectiveness and advantages of the proposed new modeling method compared to the model entirely based on the SMR method and two state-of-the-art predictive models.

Effect of initial-interfacial mixing on the amount of contamination in multiproduct pipelines

A novel approach was developed based on real conditions to calculate the amount of contamination between sequential fluids in multiproduct pipelines. Besides, the fluid shift time at the source terminal and the pipeline’s pressure gradient were used as new critical parameters for estimating initial-interfacial mixing. Also, a new correlation was presented using the experimental evaluation (i.e. monitoring the mixing growth along pipelines) and numerical modeling. The proposed equation showed acceptable results compared to the field data collected from the 55-km-long 10-in diameter and 136-km-long 16-in diameter pipelines. Increasing the initial mixing during the fluid change at the source terminal significantly enlarges the contamination.

Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines

The operation monitoring of multi-product pipelines helps to grasp the operation dynamics, detect abnormal situations in time, and assist on-site operation management. However, due to the complexity of the scheduling plan, the operating conditions of pipelines change frequently, which makes it difficult to accurately recognise condition types. To solve the above problem, an intelligent monitoring framework for operating conditions is proposed to simultaneously achieve the system recognition of steady, unsteady, and abnormal conditions. (i) The proposed monitoring framework extracts temporal and spatial characteristics of condition samples through four modules: Modules 1 and 2 form an unsupervised model for monitoring state changes and capturing temporal characteristics of condition samples; Module 3 is utilised to capture the spatial characteristics; the fusion layer based on Module 4 is applied to nonlinearly fit the spatiotemporal characteristics, and while monitoring the status changes of condition, it can also accurately recognise whether the condition is normal operation adjustment or abnormal condition. (ii) Taking a simulated pipeline and a real pipeline as examples, the effectiveness of the proposed monitoring framework is verified, and the accuracy, precision, recall, and F1 score of the recognition results reach 98.56%, 98.56%, 97.68%, and 98.12%. (iii) Through the sensitivity analysis of each module, accuracy, precision, recall, and F1 score are reduced to 96.10%, 96.10%, 95.83%, and 96.83% (i.e., only 2.46%, 2.46%, 1.85%, 1.29% differences) without Module I, which proves that the framework has strong robustness and generalisation. (iv) Finally, an intelligent analysis and control system of multi-product pipelines is designed for future applications. Consequently, the proposed intelligent monitoring framework can guide the safe operation and management of multi-product pipelines on-site.

Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines

The multi-product pipeline is the main way of refined oil transportation to ensure the safety of the energy supply. Considering that abnormal conditions of the pipeline will cause huge economic losses, personal injury, and environmental pollution, it is important to conduct abnormal detection of multi-product pipelines in time. This work proposes a deep-learning method for multi-product pipeline anomaly detection from the perspective of the temporal and spatial characteristics of the pipeline system. First, analyzing the topological structure of the real pipeline and collecting relevant equipment parameters to establish an SPS simulation pipeline system. By simulating common normal conditions and abnormal conditions in the pipeline, the pressure matrixes of each station are constructed. After being processed by the adaptive padding network and the pairing network, it is input into a customized ranking model to realize the anomaly detection of the multi-product pipeline. Finally, a simulated pipeline and a real pipeline are taken as examples to verify the effectiveness of the proposed method. The results show that the proposed hybrid model has high accuracy, precision, recall, and F1 score of 99.5%, 99.5%, 99.4%, and 99.4%, respectively, which are better than the traditional ranking model. This method can be applied to guide the safe operation and management of on-site multi-product pipelines.

Influence of multiproduct pipeline telescopicity design on the process of mixing during sequential pumping

In the present paper, the research of the processes of mixture formation is carried out using the method of transportation of light petroleum products — sequential pumping by direct contact. Modeling of the processes of mixture formation is carried out taking into account the influence of parametric factors. This paper is based on a previously published work on non-isothermal pumping of petroleum products in a pipeline. It is also worth noting that in this work, a mathematical model has been finalized to consider the increase in accuracy, namely, accounting in a telescopic pipeline. To perform numerical simulation, a software package was developed that is capable of not only modeling and calculating the parameters of the petroleum product pipeline operation in real time, followed by graphical visualization, but also comparing the obtained data with the real data processed by operators at industrial facilities.

Deeppipe: Operating Condition Recognition of Multiproduct Pipeline Based on KPCA-CNN

Operational monitoring of pipelines can prevent environmental and economic losses. However, pipeline data have the characteristics of high dimension and nonlinear coupling, which makes it difficult to determine the relationship between the data and process, resulting in a high rate of misjudgment of the operating condition. To address this issue, an operating condition recognition model based on kernel principal component analysis (KPCA)-convolutional neural network (CNN) is proposed. Deeppipe refers to the use of deep learning algorithms to solve pipeline-related problems. Considering the spatial and time-series characteristics of the pipeline, the inlet and outlet pressure matrixes of the initial station, intermediate station, and terminal station are constructed. Subsequently, the features of the pressure matrix in the time domain, frequency domain, and energy domain are extracted. KPCA is employed to obtain the reconstructed feature matrix, which is used as the input of the proposed CNN recognition model. Taking two multiproduct pipelines as examples, the effectiveness of the KPCA-CNN recognition model is verified while compared with traditional nonlinear classification models (e.g., artificial neural network, decision tree, random forest, and others). The results show that the proposed model has the highest accuracy, precision, recall, and F1 score, and all reach 100%, which has a certain guiding significance for the monitoring and management of onsite pipelines.

Crustacean waste biorefinery as a sustainable cost-effective business model

Marine-derived food wastes mainly include seafood, fish and feed production resources. From the crustaceans traded annually, 6 to 8 million tonnes of valuable shrimp, lobster and/or crab shells waste are produced worldwide. In this systemic work, the researchers with complementary technical expertise, covering the fields of chemical engineering design, chemistry, materials, predictive environmental sciences and economy, worked together to develop a sustainable multiproduct pipeline for the biorefinery of unwanted by-products. All process bio-products from the shells waste were recovered, separated, and purified. Only harmless solvents, namely water, the protonating acetic acid under mild functional conditions and buffers, conjugated with solid–liquid extraction, centrifugation, and membrane ultrafiltration technologies were applied. Here, a success business model is shown after its standardised evaluation in terms of purification performance, economic impact, and life cycle assessment has been performed, driving this sector towards a sustainable ocean-based economy.

Pipesharing: economic-environmental benefits from transporting biofuels through multiproduct pipelines

As the most potential renewable energy, biofuel energy has become the fourth largest energy source after coal, oil and natural gas. Promoting the extensive application of biofuels is an effective way to reduce carbon emissions. Pipeline is one of the most efficient ways to transport large-volume fuels over long distances. Transporting biofuels through the existing multiproduct pipelines is in line with the requirements of environmental protection, energy saving and low-carbon economy. To determine the surplus capacity of pipelines in a certain period is the premise of adopting this mode to transfer biofuels. This study develops a method for estimating the capacity of pipelines for biofuel transport, accounting for clients’ demand for petroleum products, the capacity limitations of existing pipeline equipment and transport cycle of biofuels in the pipeline. Using the data from practical pipelines, their monthly surplus capacity is within the range of 15 × 104 to 80 × 104 m3. It is turned out that pipeline length, transportation capacity of power equipment and market demand of refined products are the dominating factors for surplus capacity. Assuming varying demand on refined products, sensitivity analysis is conducted to compare the economic-environmental benefits from sharing pipeline’s surplus capacity with biofuel shipment. The simulation results show that pipeline sharing brings about win–win effects for both petroleum and pipeline companies. The petroleum company has a reduction of 1.2%–34.9% in carbon emissions and 0.3%–14.7% in logistics cost, while the pipeline operator increases revenue by 0.4%–33.7%.

Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling

It is significant for field monitoring and management to identify the operating state of the pipeline and prevent an abnormal accident from occurring. Aiming at the few unsteady and abnormal conditions data samples, different condition switching duration, and strong time-space characteristics of multi-product pipeline condition recognition, a hybrid model for condition recognition based on process and data coupling is proposed. First, collecting the relevant parameters of the real multi-product pipeline system and using Stoner Pipeline Simulator (SPS) to establish a simulation model. Based on the simulation model, performing the simulation of unsteady conditions and abnormal conditions to obtain the corresponding simulated data. Second, the reliability of the simulation model is verified by calculating the distribution similarity between the simulated data and the real pipeline data. Third, the simulated data and real data are processed by variable-length series and input into the proposed hybrid model for training and verification. The results show that the proposed hybrid model has high accuracy, precision, recall, and F1 score of 93.1%, 93.1%, 87.2%, and 84.7%, respectively, which has essential guiding significance for monitoring and ensuring the safe and stable operation of multi-product pipelines.

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.

A MILP approach for detailed pipeline scheduling and storage management problem in the phosphate industry

Short-term detailed multiproduct pipeline scheduling is a complex problem with many industry-specific constraints. We are interested in solving an operational scheduling problem of a straight pipeline in the phosphate industry. In this article, we propose a mixed-integer linear programming model (MILP) with a discrete-time formulation to provide a detailed scheduling solution for a storage-sensitive problem. The objective is to determine the charging and discharging schedule of the slurry pipeline, to satisfy products demand while respecting all pipeline and storage constraints. The model was tested on different instances, proving to be able to minimize products sold-outs and the total water quantity scheduled.

A robust hybrid predictive model of mixed oil length with deep integration of mechanism and data

Accurate estimation of mixed oil length is highly required in multi-product pipelines because it can guide the operator to correctly handle the mixed oil segment and effectively reduce the loss of petroleum product quality. In previous study, a hybrid model combined with machine learning algorithm with existing mechanism has been developed and has good predictive accuracy. Unfortunately, due to incorrect measurement and improper recording, outliers are widely present in industrial datasets and may render the predictive performance of the previous model quite disappointing, while the effect of outliers on predictive models for the mixed oil length is rarely discussed. In order to deal with such issues, this paper first proposes a way to define the outlier sample and explicitly studies its impact on the performance of the predictive model for mixed oil prediction. Subsequentially, various new hybrid modeling methods are developed driven by both operation data (exploited by the Gradient Boosting Decision Tree algorithm) and the mechanism (based on the Austin-Palfrey equation) in different arrangements. Extensive experiments are conducted on real-life transportation pipelines, and the results show that with the clean training set, the R2 index of the proposed serial-parallel hybrid model (SPHM) is 0.96, which is higher than that of mechanism model and the existing hybrid model. Even with all the outliers added, advantage in prediction accuracy of the SPHM is still noticed, demonstrating feasibility and robustness of the hybrid modeling approach for prediction of mixed oil length.

Modification Of Various Product Pipeline System: Optimization and Scheduling

The optimal scheduling system of multi product pipelines is benefited to improve the economy and safety transportation. This paper addresses how to optimize detailed schedules of a multi-product pipeline. The study covers multi fluid pipeline by national pipeline company which is 130 Km. We concern with batching of (Gas oil, Jet fuel, Naphtha, Gasoline 80, Gasoline 95) and here comes the problem of contamination of one fluid with another which is leading fluid. This study estimates the volume of mixing zone for each interface by equation of Austin J.E and palfrey J.R, determines contaminated volume cost and storage volume needed for each cycle and assures that transportation is within flat zone and yield minimum value of interface volume providing the optimum multi-product pipeline scheduling.

Scheduling of a branched multiproduct pipeline system with robust inventory management

As the main transportation mode of refined products, multiproduct pipelines play an important role in ensuring the downstream energy supply. Before the market-oriented reform of refined products, the construction and management of multiproduct pipelines were monopolized by giant state-owned enterprises, forming the self-operation mode. However, with the development of refined products market, the market-oriented operation mode has gradually formed. Under the new mode, the market demand presents a stronger uncertainty, resulting in the scheduling method under self-operation mode may not be able to cope with the supply interruption risk caused by demand uncertainty. The robustness of the obtained schedule is low. Aiming at above issue, this paper proposes a method for scheduling of branched multiproduct pipeline system under market-oriented mode. The method considers market demand uncertainty and robust inventory management. It can not only reduce additional operation cost caused by demand uncertainty, but also decrease the adjustment frequency of schedule, so that the schedule is still feasible within a certain fluctuation range. Finally, the proposed model is validated by applying it to a real-world multiproduct pipeline system.

Systematic Approaches for Optimal Scheduling of Straight Pipelines with Batch-Centric Formulations

Scheduling of the transportation of liquids by multiproduct pipelines has been receiving considerable attention in the literature. The preferred methods for solving this problem rely on mathematical programming and use continuous-time formulations that move the virtual entities of batches instead of the real entities of products. Such batch-centric formulations require the user to convert the products initially in the pipeline into batches, which is trivial to do for linear or branched systems with a single input node but not for straight systems with multiple input nodes. While the dependence of solution quality on the initial batch sequence has been recognized early, no systematic procedure covering all possibilities has been presented yet. In this paper, we propose a novel scheduling algorithm that starts generating all possible initial batch sequences with a new planning formulation, before exploring all generated sequences with either a sequential or simultaneous approach, using the scheduling formulation of Liao, Q.; [ AIChE J. 2019, 65, e16712]. We then generalize this formulation to consider automatic batch selection, by including the logical constraints of the planning model.