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Abstract Water pumping systems are widely used in industrial and civil applications. During their operational life, they consume energy and materials (mainly for installation and replacement of components). The aim of this paper is to optimize the pump operations in order to save energy and enlarge the operational life of the pumps and their components. This paper addresses the multi-period (e.g. 24-hours time horizon) optimization of pumping systems. To this end, we have developed a simulation-based optimization approach including novel relevant technical features of a generic but realistic pumping system, namely cavitation and overflow. The proposed multi-period optimization differs further from the classic static optimization (i.e. at the design stage), proposing a new dynamic approach in which pump activation is steered dynamically for an optimal management of the variability of water inflow. Furthermore, the problem includes constraints on the number of pump activations allowed to reduce material strain. The performances of different meta-heuristic optimization algorithms (e.g. genetic algorithm, simulated annealing and particle swarm optimization) for solving the problem are compared. The numerical results show that energy savings are possible with the dynamic approach and that particle swarm optimization and simulated annealing algorithms provide the most suitable solutions for this problem. Highlights • Three algorithms are compared to minimize energy pump consumption. • The pump model includes energy, cavitation, overow and activation frequency. • Static and dynamic optimizations are investigated. • The dynamic optimization can reduce the energy consumption by 6.5%. [ABSTRACT FROM AUTHOR]

Coal mining activities have caused serious environmental problems; however, almost 30% of global primary energy demand is met using coal, making it difficult to replace in the near future. Under these circumstances, as it is not possible to completely cease coal mining activities, coal mines need to invest in cleaner production techniques to reduce environmental damage. To achieve pollution emissions and energy reductions, this paper proposes a dynamic multi-objective mixed 0–1 model based on ecological coal mining, in which environmental technological investment and production adjustments are integrated into a dynamic system to seek ecological and economic equilibrium, and emissions reductions and energy conservation are controlled using double dynamic transfer equations. Different from previous papers, this model considers the effect of production adjustments and energy conservation on the environment and the economy with the aim of achieving global optimization through integrated planning. To solve the proposed model, a hybrid algorithm with a standard and an antithetic method-based Particle Swarm Optimization (PSOs) is developed. A case study from Chaohua Colliery is presented to demonstrate the practicality and efficiency of the optimization model. The results and comparison analyses showed that as the dynamic adjustments strengthened the production and investment coordination for global optimization, integrated production and environmental investment planning provided coal mines with a superior method to solve conflicts between the ecology and the economy. [ABSTRACT FROM AUTHOR]

This paper aims to present a novel smart grid adaptive energy conservation and optimization engine for smart distribution networks. The optimization engine presented in this paper tries to minimize distribution network loss, improve voltage profile of the system and minimize the operating cost of reactive power injection by switchable shunt Capacitor Banks using Advanced Metering Infrastructure data. Moreover, it performs Conservation Voltage Reduction (CVR) and minimizes transformer loss. To accurately weight the optimization engine objective function sub-parts, Fuzzification technique is employed in this paper. Particle Swarm Optimization (PSO) is applied as Volt-VAR Optimization (VVO) algorithm. Substantial benefits of the proposed energy conservation and optimization engine include but not limited to: adequate accuracy and speed, comprehensive objective function, capability of using AMI data as inputs, and ability to determine weighting factors according to the cost of each objective sub-part. To precisely test the applicability of proposed engine, 33-node distribution feeder is used as case study. The result analysis shows that the proposed approach could lead distribution grids to achieve higher levels of optimization and efficiency compared with conventional techniques. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoTs) network life is shortened due to sensor node (SN) energy limitations and computational capability. As a result, optimum node location estimation and efficient energy usage are two critical IIoT requirements. This work reduces energy consumption by performing node localization and cluster-based routing using an improved evolutionary algorithm called Cat Swarm Optimization (CSO). First, the CSO method is used to optimize the bio-inspired node's location. Second, to conserve SN energy in the IIoT network, a cluster-based routing technique is used. The objective function is defined as minimizing the average distance between the cluster and its SNs while selecting the most energy-efficient Cluster Head (CH). In terms of fitness value, the Improved CSO (ICSO) algorithm outperforms the Particle Swarm Optimization (PSO) algorithm. In this paper, real-time test-bed analysis was used to investigate the performance of both node localization and energy-efficient clustering. When it comes to achieving sustainable IIoT and green cities, the findings show that ICSO outperforms in terms of convergence rate and network lifetime. • An algorithm based on Improved Cat Swarm Optimization for Sustainable IIoT Networks and Green Cities. • An optimized IIoT framework has increased the convergence rate and network lifetime. • The proposed bio-inspired localization technique uses RSS-distance conversion for IIoT. • The proposed technique adopts a centralized cluster-based protocol for optimal routing in IIoT. [ABSTRACT FROM AUTHOR]

Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes' closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively. [ABSTRACT FROM AUTHOR]