An Optimization-Based System Dynamics Simulation for Sustainable Policy Design in WEEE Management Systems

The increase in the use of electrical and electronic devices worldwide has created a rapid growth of waste of electrical and electronic equipment (WEEE). The current paper presents an optimization-based simulation (OBS) approach that allows the design of sustainable WEEE management system policies. The proposed OBS approach integrates a system dynamics (SD) model and a mixed-integer nonlinear programming (MINLP) model to improve the representation and performance of the WEEE processes considering their operative and strategic interdependence. The SD component elicits the complexity of the WEEE generation process. Complementarily, the MINLP model periodically optimizes key variables of the WEEE management system. Computational results in a case study based on WEEE from Colombian mobile phones illustrates how an approach solely based on SD simulation is unable to capture the operative-strategic nature of the system and perform optimal parameter updates. By contrast, the OBS approach of this paper outperforms an exclusive SD analysis both in the economic and environmental performance of the system. It obtains 33% more profits and 65% more environmental benefits. Moreover, for this case study, the model suggests that the cornerstone of the WEEE management system for increasing its performance is the replacement rate.

Optimal Design and Scheduling of Offshore Oil and Gas Field Development with Environmental Impact Consideration

The objective of the paper is to develop a mixed integer nonlinear programming (MINLP) model for optimum design and scheduling of offshore oil and gas field development in respect to simultaneous consideration of economic and environmental impact. The model is utilized as a tool for decision making management in conceptual stage. Nonlinear reservoir behavior and floating demand constraint are incorporated to improve accuracy of the solution. This paper utilizes mathematical programming techniques to address the design and scheduling problem of offshore oil and gas field development. Field development problem is first formulated into a multi-objective MINLP model incorporating many realistic features such as nonlinear reservoir behavior and floating demands. The objectives are to maximize net present value (NPV) and minimize total environmental impact (TEI) simultaneously. Environmental impact is assessed using the ReCiPe2016 method. Augmented ε-constraint method (AUGMECON) is then employed to solve the proposed multi-objective MINLP model to generate the Pareto-optimal front that is able to assist decision maker selecting the most preferred solution. The performance of the proposed modelling framework is investigated on a set of problem which consists of 2 reservoirs, 2 FPSOs, 2 customers and 5-years planning horizon. First model with single objective function to maximize NPV can be solved effectively within short computational time. The solution gives optimum decision of design, investment, production schedule, and transportation regardless the environmental impact. Then, simultaneous optimization of multi-objective MINLP with different value of ε-constraint generates multiple development schemes and objective function values. The results indicate trade-off between maximizing NPV and minimizing TEI. It is possible to obtain maximum NPV of USD 2.4 trillion at the expense of TEI which is 307.518 or to generate minimum TEI of 16.65 at the expense of NPV which is USD 74.368 billion. All possible solutions within extreme values range are presented in form of a Pareto-optimal front where TEI and NPV are plotted in x and y-axis respectively. It will assist the company to select the most preferred solution based on NPV. Consequently, the selected option brings corresponding value of TEI. Additionally, the Pareto optimal front also allows decision maker to have more flexibility to compromise between economic and environmental issues. This is the first study to consider environmental impact in the offshore oil and gas field development. Many realistic operational features such as nonlinear reservoir behavior and floating demands are also incorporated. In addition to that, the proposed framework yields a powerful tool to assist decision maker selecting the most preferred solution that satisfies their criteria in both economic and environmental aspects.

Feeder Scheduling and Container Transportation with the Factors of Draught and Bridge in the Yangtze River, China

Inland shipping in the Yangtze River in China has become very prosperous, making feeder scheduling and container transportation increasingly difficult for feeder operators. This research analyzed the decision-making of container transportation businesses in feeder companies operating between Shanghai Port and inland ports along the Yangtze River in China. The research considered the complexity of the natural conditions and water channels, including the draught limitations and the height of the bridges over the river. To analyze ways to increase the effectiveness of shipping containers from Shanghai Port into inland river ports along the Yangtze River, we built a mixed integer nonlinear programming (MINLP) model to minimize the total operating cost and determine the most effective departure time of each feeder. After linearizing the model, we designed a particle swarm optimization (PSO) algorithm to increase solution efficiency and introduced a taboo list and aspiration criterion of a Taboo Search (TS) algorithm to improve the PSO algorithm. Finally, we verified the accuracy of the model and the efficiency of the algorithms using numerical experiments. The research provides theoretical guidance for feeder operators and inland river shipping companies.

A Scatter Search Algorithm for Multi-Criteria Inventory Classification with Maximize the Satisfaction Level

Inventory management requires thousands or millions of individual transactions each year. Classification of the items influences the results of inventory management. Traditionally, this is usually classified with considering an annual dollar usage criterion but maybe other criterias such as lead time, criticality, perishability, inventory cost, and demand type can be affected on that classification. The objective of this study is to determine the multi-criteria inventory classification (MCIC) of the inventory items to minimize the total inventory cost and also dissimilarity of classes. Because of the two objectives is considered to solve with together, the maximization of satisfaction level is described to solve the multi-objective problem. This study introduces a Mixed Integer Nonlinear Programming (MINLP) model of the MCIC problem by giving two objectives. A Scatter Search Algorithm (SSA) is used to solve the MINLP model for obtaining high-quality solutions within reasonable computation times. Finally, we illustrate an example and compare our results with other studies in previous literature.

Multi-Energy Flow Calculation Considering the Convexification Network Constraints for the Integrated Energy System

To alleviate environmental pollution and improve the energy efficiency of end-user utilization, the integrated energy systems (IESs) have become an important direction of energy structure adjustment over the world. The widespread application of the coupling units, such as gas-fired generators, gas-fired boilers, and combined heat and power (CHP), increases the connection among electrical, natural gas, and heating systems in IESs. This study proposes a mixed-integer nonlinear programming (MINLP) model combining electrical, natural gas, and heating systems, as well as the coupling components, such as CHP and gas-fired generators. The proposed model is applicable for either the radial multi-energy network or the meshed multi-energy network. Since the proposed MINLP model is difficult to be solved, the second-order cone and linearized techniques are used to transform the non-convex fundamental matrix formulation of multi-energy network equations to a mixed-integer convex multi-energy flow model, which can improve the computational efficiency significantly. Moreover, the potential convergence problem of the original model can also be avoided. A simulation of IEEE 14-node electrical system, 6-node natural gas system, and 23-node heating system are studied to verify the accuracy and computational rapidity of the proposed method.

Crystallization Solvent Design Based on a New Quantitative Control Model of Crystal Morphology

Solution crystallization is an important separation unit operation in active pharmaceutical ingredient (API) production. Solvent is one of the important factors affecting crystal morphology. How to select/design suitable crystallization solvents is still one of the most urgent problems in the crystallization field. In this paper, a framework for crystallization solvent design based on the developed quantitative control model of crystal morphology is proposed. First, molecular dynamics is used to predict the crystal morphology in solvents. Next, nine solvent descriptors are selected. Then, the quantitative relationship between crystal aspect ratio and solvent descriptors is developed. Subsequently, Computer-Aided Molecular Design (CAMD) method is integrated with the developed quantitative control model. The crystallization solvent design problem is expressed as a Mixed-Integer Non-Linear Programming (MINLP) model, which is solved by the decomposition algorithm. Finally, the crystallization solvent design framework is applied to two cases: benzoic acid and ibuprofen, and experimental verification is implemented.

Exact minimization of the energy losses and the CO2 emissions in isolated DC distribution networks using PV sources

This paper addresses the optimal location and sizing of photovoltaic (PV) sources in isolated direct current (DC) electrical networks, considering time-varying load and renewable generation curves. The mathematical formulation of this problem corresponds to mixed-integer nonlinear programming (MINLP), which is reformulated via mixed-integer convex optimization: This ensures the global optimum solving the resulting optimization model via branch & bound and interior-point methods. The main idea of including PV sources in the DC grid is to minimize the daily energy losses and greenhouse emissions produced by diesel generators in isolated areas. The GAMS package is employed to solve the MINLP model, using mixed and integer variables; also, the CVX and MOSEK solvers are used to obtain solutions from the proposed mixed-integer convex model in the MATLAB. Numerical results demonstrate important reductions in the daily energy losses and the harmful gas emissions when PV sources are optimally integrated into DC grid.

MINLP Model for Operational Optimization of LNG Terminals

Liquefied natural gas (LNG) is a clear and promising fossil fuel which emits less greenhouse gas (GHG) and has almost no environmentally damaging sulfur dioxide compared with other fossil fuels. An LNG import terminal is a facility that regasifies LNG into natural gas, which is supplied to industrial and residential users. Modeling and optimization of the LNG terminals may reduce energy consumption and GHG emission. A mixed-integer nonlinear programming model of the LNG terminal is developed to minimize the energy consumption, where the numbers of boil-off gas (BOG) compressors and low-pressure (LP) pumps are considered as integer variables. A case study from an actual LNG terminal is carried out to verify the practicality of the proposed method. Results show that the proposed approach can decrease the operating energy consumption from 9.15% to 26.1% for different seasons.