A hybrid time MILP model for the pump scheduling of multi-product pipelines based on the rigorous description of the pipeline hydraulic loss changes

Optimization of petroleum products distribution via pipeline systems: Modeling and computational challenges

Optimizing the distribution of refined oil products using pipeline systems has been posing a meaningful challenge to the operations research field of exceptional economic importance for the oil and gas industry. The solution of this problem stands on the interface of chemical engineering and operations research, the former has been the most important contributor, and the latter has been paying an increasing amount of attention to its solution over the last ten years or so. The goal of this work is fourfold: to unveil the current shape of the accomplished research work on this topical area according to its descriptive analytics, to present and discuss its modeling research perspectives, to outline its emerging research trends and to trigger discussions on its future research avenues. A future research agenda should study more realistic mathematical models for the system and solution procedures that exploit their algebraic structure.

The Data-Driven Modeling of Pressure Loss in Multi-Batch Refined Oil Pipelines with Drag Reducer Using Long Short-Term Memory (LSTM) Network

Due to the addition of the drag reducer in refined oil pipelines for increasing the pipeline throughput as well as reducing energy consumption, the classical method based on the Darcy-Weisbach Formula for precise pressure loss calculation presents a large error. Additionally, the way to accurately calculate the pressure loss of the refined oil pipeline with the drag reducer is in urgent need. The accurate pressure loss value can be used as the input parameter of pump scheduling or batch scheduling models of refined oil pipelines, which can ensure the safe operation of the pipeline system, achieving the goal of energy-saving and cost reduction. This paper proposes the data-driven modeling of pressure loss for multi-batch refined oil pipelines with the drag reducer in high accuracy. The multi-batch sequential transportation process and the differences in the physical properties between different kinds of refined oil in the pipelines are taken into account. By analyzing the changes of the drag reduction rate over time and the autocorrelation of the pressure loss sequence data, the sequential time effect of the drag reducer on calculating pressure loss is considered and therefore, the long short-term memory (LSTM) network is utilized. The neural network structure with two LSTM layers is designed. Moreover, the input features of the proposed model are naturally inherited from the Darcy-Weisbach Formula and on adaptation to the multi-batch sequential transportation process in refined oil pipelines, using the particle swarm optimization (PSO) algorithm for network hyperparameter tuning. Case studies show that the proposed data-driven model based on the LSTM network is valid and capable of considering the multi-batch sequential transportation process. Furthermore, the proposed model outperforms the models based on the Darcy-Weisbach Formula and multilayer perceptron (MLP) from previous studies in accuracy. The MAPEs of the proposed model of pipelines with the drag reducer are all less than 4.7% and the best performance on the testing data is 1.3627%, which can provide the calculation results of pressure loss in high accuracy. The results also indicate that the model’s capturing sequential effect of the drag reducer from the input data set contributed to improving the calculation accuracy and generalization ability.

Energy saving and management of water pumping networks

The main consumption of energy in water systems is the pumps. Due to the different tariff of energy consumption during the one day, the operation of these pumps should be controlled to minimize their consumption and consequently decrease the cost of operation. This paper utilizes an optimization algorithm to control the on/off operation of water pumps to minimize the cost of energy consumption and number of pump switching of water networks. This objective function is subjected to some optimization and hydraulic constraints such as the tanks upper and lower limits, and water network pressure limit. The proposed methodology is an iterative combination process between an optimization algorithm and EPANet hydraulic simulator where optimization algorithm generates the schedules and the hydraulic simulator is used to check the feasibility of these schedules. The suggested optimization method is the artificial electric field algorithm (AEFA). This methodology is applied to three water networks; EPANet practical example network, Richmond network and a part from Toronto network with a variable energy consumption tariff. The AEFA is tested and trained to select the best values of its controlling parameters for each network. The results show that the energy consumption cost is significantly decreased by the optimal schedules of water pumps. Also AEFA is compared with other optimization algorithms such as the genetic and particle swarm algorithms on the same networks and energy tariff and the results show the superiority of AEFA in the convergence and saving of the cost of energy consumption.