Solar panel photovoltaic (PV) systems are widely used in Korea to generate solar energy, which is one of the most promising renewable energy sources. With regard to solar electricity providers and a grid operator, it is critical to accurately predict solar power generation for supply–demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because solar power generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for solar power generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of solar radiation as well as a loss adjustment factor to reflect the possible loss of power generation due to the degradation or failure of the PV module. Using the proposed model, solar power generation for the following day can be predicted. The proposed model is embedded into a solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models.

The present study investigated fluorene biodegradation by Sphingobacterium sp. KM-02 in the presence of heavy metals. A fluorene-degrading strain of Sphingobacterium was isolated from polycyclic aromatic hydrocarbon-contaminated soil near a mine-impacted area in Korea. When Sphingobacterium sp. KM-02 was grown in a medium with fluorene as the sole carbon source, it removed 78.4% of this compound within 120 h. Identification of the metabolic intermediates (9-fluorenone, 4-hydroxy-9-fluorenone, and 8-hydroxy-3,4-benzocoumarin) were also performed. Composting experiments under laboratory conditions indicated that this microbe also removed fluorene from contaminated soil. In particular, treatment of microcosm soil with strain KM-02 for 20 days resulted in a 65.6% reduction of fluorene concentration. Experiments of effects of heavy metals on fluorene removal by strain KM-02 showed that 10 mg/L cadmium, copper, zinc, and lead, reduced growth and fluorene degradation by this microbe. Cadmium and copper had strong effects at 10 mg/L, although zinc and lead had relatively slight inhibitory effect at concentrations of 10 and 100 mg/L. Arsenic had no effect on the growth or fluorene degradation, even at 100 mg/L.

In many manufacturing or service industries, there exists maximum allowable tardiness for orders, according to purchase contracts between the customers and suppliers. Customers may cancel their orders and request compensation for damages, for breach of contract, when the delivery time is expected to exceed maximum allowable tardiness, whereas they may accept the delayed delivery of orders with a reasonable discount of price within maximum allowable tardiness. Although many research works have been produced on the job shop scheduling problem relating to minimizing total tardiness, none of them have yet considered problems with maximum allowable tardiness. In this study, we solve a job shop scheduling problem under maximum allowable tardiness, with the objective of minimizing tardiness penalty costs. Two kinds of penalty costs are considered, i.e., one for tardy jobs, and the other for canceled jobs. To deal with this problem within a reasonable time at actual production facilities, we propose several dispatching rules by extending well-known dispatching rules for the job shop scheduling problem, in cooperation with a probabilistic conception of those rules. To evaluate the proposed rules, computational experiments were carried out on 300 test instances. The test results show that the suggested probabilistic dispatching rules work better than the existing rules and the optimization solver CPLEX, with a time limit.

We study the ship speed optimization problem with the objective of minimizing the total fuel consumption. We consider multiple time windows for each port call as constraints and formulate the problem as a nonlinear mixed integer program. We derive intrinsic properties of the problem and develop an exact algorithm based on the properties. Computational experiments show that the suggested algorithm is very efficient in finding an optimal solution.

The shipping route network design is a strategic decision issue for shipping lines because liner ships operate along routes and timetables published months ahead. This paper considers an Integrated Planning Problem (IPP) of determining the speed, route, and number of ships deployed (fleet size) of a ship type. The objective is to maximize a carrier’s profit, which is the revenue minus Cargo Handling Charge (CHC), bunker consumption cost, port charge, cargo inventory cost and ship time cost. We present a nonlinear shortest-path model to represent the considered problem and suggest a Simulated Annealing (SA) algorithm with some analytic methods. Performance evaluation experiments demonstrate that the proposed algorithm works well for small test problems. A number of scenario analyses are further performed with the heuristic algorithm to investigate the effect of exogenous factors, including bunker price, chartering cost, revenue (sea freight rate) and ship size. The analysis results show that strategic decisions for determining the route, fleet size and ship speed can be affected by changes in the factors. The performance tests and scenario analyses show that the heuristic algorithm can be utilized as an efficient and effective planning tool for the liner service in both the current environment and feasible future situations.

In this study, we conducted preliminary research with the objective of leveraging artificial intelligence to optimize the efficiency and safety of the entire Ambient Air Vaporizer (AAV) system for LNG (Liquid Natural Gas). By analyzing a year-long dataset of real operational data, we identified key variables that significantly influence the outlet temperature of Natural Gas (NG). Based on these insights, a Deep Neural Network (DNN) prediction model was developed to forecast the NG outlet temperature. The endeavor to create an effective prediction model faced specific challenges, primarily due to the narrow operational range of fan speeds and safety-focused guidelines. To surmount these obstacles, various learning algorithms were evaluated under multiple conditions. Ultimately, a DNN model exhibiting lower values of both absolute mean error (MAE) and mean square error (MSE) was successfully established.