Genetic algorithms (GAs) are used to optimize solutions to problems, particularly those that are analytically impossible to solve. As their name suggests, they are inspired by the biological concepts of genetics and evolution. Our work aims to study and model a silicon-based photovoltaic generator (PVG). Among the various models available is that of the diode. Modeling was used to approximate the PVG output (voltage, current) as a function of two inputs: temperature and irradiation. The parameters of our model were identified using a real coding algorithm, with the cumulative square error was used for selection. To test the effectiveness of our model, we carried out simulation tests on the power-voltage (P-V) and current-voltage (I-V) characteristics of a wide range of irradiation and temperature variations. This study demonstrates the effectiveness and accuracy of the proposed approach (GAs) and validates the parameters obtained and used in the single-diode electrical model. The results indicate that the GA technique is a better conventional parameter extraction strategy in terms of convergence. It provides globally optimal solutions.

The amount of waste that contains or is contaminated with radionuclides is increasing gradually due to the use of radioactive material in various fields including the operation and decommissioning of nuclear facilities. Such radioactive waste should be safely managed until its disposal to protect public health and the environment. Predisposal management of radioactive waste covers all the steps in the management of radioactive waste from its generation up to disposal, including processing (pretreatment, treatment, and conditioning), storage, and transport. There could be a lot of strategies for the predisposal management of radioactive waste. In order to comply with safety requirements including Waste Acceptance Criteria (WAC) at the radioactive waste repository however, the optimal scenario must be derived. The type and form of waste, the radiation dose of workers and the public, the technical options, and the costs would be taken into account to determine the optimal one. The time required for each process affects the radiation dose and respective cost as well as those for the following procedures. In particular, the time of storing radioactive waste would have the highest impact because of the longest period which decreases the concentrations of radionuclides but increases the cost. There have been little studies reported on optimization reflecting variations of radiation dose and cost in predisposal management scenarios for radioactive waste. In this study, the optimal storage time of radioactive waste was estimated for several scenarios. In terms of the radiation dose, the cumulative collective dose was used as the parameter for each process. The cost was calculated considering the inflation rate and interest rate. Since the radiation dose and the cost should be interconvertible for optimization, the collective dose was converted into monetary value using the value so-called “alpha value” or “monetary value of Person-Sv”.

When decommissioning a nuclear power plant, a large amount of radioactive waste is generated simultaneously. Therefore, efficient treatment of radioactive waste is crucial to the success of the decommissioning process. An utility or decommissioning contractor of NPP often build separate radioactive waste treatment facilities (RWTF) to handle this waste. In Korea, RWTFs are planned to be built for the decommissioning of the Kori Unit 1 and Wolsong Unit 1. In this study, we introduce an application case of using process simulation to derive the optimal layout design and investment plan for a radioactive waste treatment facility. In particular, the steam generator is the largest and most complex device processed in RWTF. Therefore, it is necessary to reflect the large equipment processing area that can treat steam generators in the design of RWTF. In this study, Siemens’ Plant Simulation® was used to derive an optimization plan for the dismantling area of large equipment in RWTF. First, a virtual facility was built by modeling based on the steam generator dismantling process and facilities developed by Doosan Enerbility. This was used to pre-validate the facility investment plan, discover wasteful factors in the logistics waste streams, and evaluate alternatives to derive, validate, and apply appropriate improvement alternatives. Through this, we designed a layout based on the optimal logistics waste streams, appropriate workstations, and the number of buffer places. In addition, we propose various optimization measures such as investment optimization based on optimal operation of facility resources such as facilities and manpower, and establishment of work standards.

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a Decision Tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 °C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about − 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.

본 논문은 교정보호체계 내 대상자 수 적정화 필요성과 그 방향에 대해 다룬다. 범 죄자의 실효적 재범방지를 위해서는 증거기반 정책의 수립과 집행체계의 전문성 강화 등 다양한 이슈가 논의 될 수 있다. 다만, 이러한 정책들이 성공하기 위해서는 정책의 집행대상을 명확히 하여 밀도 높은 교정교화 활동을 실시 할 필요가 있다. 이러한 관 점에서, 우리 교정보호체계가 가진 큰 문제점은 교정과 보호체계 모두 필요 이상의 많 은 범죄자들을 관리감독하고 있다는 점에 있다. 교정시설의 과밀수용현상과 보호관찰 소의 만성적 인력부족 현상은 이러한 현상의 단면을 잘 보여준다. 우리 교정보호체계 는 이러한 문제점을 시설과 인력의 확충이라는 방법을 통해서 해결하려 해왔다. 그러 나 교정보호체계가 각자의 몸집을 불려가는 망의 확장 현상은 귀중한 형사사법 자원 의 낭비를 초래할 뿐 아니라, 최근 가장 설득력 있는 교정이론 중 하나인 RNR이론에 따르면 재범률 감소에 긍정적 영향을 주지 못한다. 교정과 보호 두 기관은 지역사회안전을 해치지 않는 범위내에서 대상자의 수를 감소시킴으로써 근본적인 문제를 해결 해야 한다. 시설내 교정체계는 지금 보다 많은 수용자를 탈 시설화(decarceration) 하여야 하고, 사회내 처우체계도 재범위험성이 낮은 범죄자를 조기해방(early release) 시켜줘야 한다. 본 논문은 한국의 교정보호체계가 전체 교정보호 대상자 총량의 감소 를 통해 스마트한 교정보호체계로 거듭날 수 있도록, 실효적 정책대안과 향후 제언을 제시하였다.

A coagulation-flocculation (CF) process using aluminum sulfate as a coagulant was employed to treat highly suspended solids in tunnel wastewater. Response surface methodology (RSM) based on a Box-Behnken design was applied to evaluate the effects of three factors (coagulant dosage, pH and temperature) on total suspended solids (TSS) removal efficiency as well as to identify optimal values of those factors to maximize removal of TSS. Optimal conditions of coagulant dosage and pH for maximum TSS removal changed depending on the temperature (4 ~ 24°C). As temperature increased, the amount of coagulant dosage and pH level decreased for maximum TSS removal efficiency during the CF process. Proper adjustment of optimal pH and coagulant dosage to accommodate temperature fluctuations can improve TSS removal performance of the CF process.

This paper proposes a computation model of the quantity supplied to optimize inventory costs for the fast fashion. The model is based on a forecasting, a store and production capacity, an assortment planning and quick response model for fast fashion retai

비동결 파운드 케이크의 배합비를 반응표면분석법에 의해 여러 가지 동결방지제의 함량을 조절하여 결정하였다. 수분함량만이 동결방지제 함량과 선형관계를 나타내었으나, 다른 반응변수들은 quadratic모형으로 나타났다. 비동결 파운드 케이크에 제조시 함유되는 당류의 최적 배합비는 7736 : 0.0 : 20.4, 60.0 : 32.8 : 16.4, 70.8 : 11.2 : 19.6와 같이 3가지로 제시되었다. 최적배합비로 제조한 파운드 케이크의 냉동