Material flow analysis and integration of watersheds and drainage systems: I. Simulation and application to ammonium management in Bahr El-Baqar drainage system

This paper is aimed at developing a systematic and generally applicable methodology for material flow analysis in drainage systems and watersheds. In particular, this research has focused on developing a mathematical framework and application for the management of nitrogenous species (primarily ammonium ions). Nitrogen compounds are among the most important species contributing to ecological cycles. Indeed, the environmental and biological aspects of water systems and their surrounding systems are highly impacted by nitrogen compounds as they contribute to the quality, nutrition, and toxicity of these systems. A material flow model was developed to deal primarily with the water phase while including pertinent information on the solid and air phases as they interface with the water medium. Both spatial and discrete temporal dimensions were included to account for nitrogen flow and transformation. The model includes the various environmental phenomena that influence the fate and transport of targeted species (e.g., volatilization, precipitation, sedimentation, uptake by biota, adsorption, chemical and biochemical reactions, etc.). Furthermore, the model includes material flow analysis operators (or transfer functions) that characterize the system inputs and outputs as they relate to the surroundings. The aforementioned material flow analysis tools were combined in a computer-aided modeling platform to provide a complete material flow analysis and yield useful insights on the transport and fate of targeted species. The simulation results shed light on the system performance. Actual data for an Egyptian drainage system (Bahr El-Baqar) along with the outfall to Lake Manzala were used to illustrate the usefulness and applicability of the developed model. Comparison with the measured data confirmed the validity and fidelity of the model.     

Environmental-impact reduction through simultaneous design, scheduling, and operation

Processing facilities are normally designed with sufficient flexibility to handle nominal variations. When the process features planned changes in feedstock and products, scheduling is often used to optimize process operation. Proper scheduling may be limited to existing design or may entail retrofitting. Traditionally, economic objectives have served as the primary drivers for the design, retrofitting, and scheduling of industrial processes. Once a base design and scheduling plan have been established, environmental issues are addressed in many cases as an afterthought. As a result of this sequential approach, valuable synergisms and tradeoffs of economic and environmental objectives are often missed. The objective of this study is to develop a new approach to design and scheduling with economic and environmental objectives. Specifically, this study introduces a systematic framework and the associated mathematical formulation for simultaneous process design and scheduling while simultaneously addressing economic and environmental objectives. Therefore, this study establishes two types of proper tradeoffs (a) between design and scheduling and (b) between economic and environmental objectives. The environmental issues pertaining to the parameterized process retrofitting, scheduling, and operation strategies are simultaneously considered along with the environmental impact of these changes. An optimization formulation is developed for the case of project schedule while allowing design retrofitting changes that include new environmental units and modification of design and operating conditions in the process (without new process units). Also, a process model with the appropriate level of relevant details is included in the formulation. The projected schedule is discretized to allow for a multiperiod formulation with algebraic equations. The resulting framework identifies opportunities for synergism between the economic and environmental objectives. It also determines points of diminishing return beyond which tradeoffs between economic and environmental objectives are established. The devised procedure is illustrated with a case study on an oil refinery with scheduling of different products and the design of an environmental system that addresses NO _x emission.     

A methodological framework for the triple bottom line accounting and management of industry enterprises

Since the 1970s the purpose of the environmental protection campaign has been to try to halt the gradual deterioration of our natural habitat. Reactive and pro-active strategies were developed to incorporate environmental and social concerns into economic development beyond regulatory compliance. In this research, we present a methodology to support the new era of sustainable development for all the three areas, which implements the triple bottom line (TBL) accounting mechanism by using quantitative models. A broad TBL framework is developed to track and categorize sustainability information at the corporate level through a sustainability index system. A ‘sustainability optimization’ model incorporates environmental and social costs and values into economic activities to support the decisions of the management. This methodology can help decision makers to make ‘green’ plans, and provide strategic directions for future development.     

Multi-objective optimization of process cogeneration systems with economic, environmental, and social tradeoffs

Process cogeneration is an effective strategy for exploiting the positive aspects of combined heat and power in the process industry. Traditionally, decisions for process cogeneration have been based mostly on economic criteria. With the growing interest in sustainability issues, there is need to consider economic, environmental, and social aspects of cogeneration. The objective of this article is to develop an optimization framework for the design of process cogeneration systems with economic, environmental, and social aspects. Process integration is used as the coordinating framework for the optimization formulation. First, heat integration is carried out to identify the heating utility requirements. Then, a multi-header steam system is designed and optimized for inlet steam characteristics and their impact on power, fixed and operating costs, greenhouse gas emissions, and jobs. A genetic algorithm is developed to solve the optimization problem. Multi-objective tradeoffs between the economic, environmental, and social aspects are studied through Pareto tradeoffs. A case study is solved to illustrate the applicability of the proposed procedure.     

Hybrid-Trefftz six-node triangular finite element models for Helmholtz problem

In this paper, six-node hybrid-Trefftz triangular finite element models which can readily be incorporated into the standard finite element program framework in the form of additional element subroutines are devised via a hybrid variational principle for Helmholtz problem. In these elements, domain and boundary variables are independently assumed. The former is truncated from the Trefftz solution sets and the latter is obtained by the standard polynomial-based nodal interpolation. The equality of the two variables are enforced along the element boundary. Both the plane-wave solutions and Bessel solutions are employed to construct the domain variable. For full rankness of the element matrix, a minimal of six domain modes are required. By using local coordinates and directions, rank sufficient and invariant elements with six plane-wave modes, six Bessel solution modes and seven Bessel solution modes are devised. Numerical studies indicate that the hybrid-Trefftz elements are typically 50% less erroneous than their continuous Galerkin element counterpart.     

Sustainable development of process facilities: state-of-the-art review of pollution prevention frameworks

Pollution prevention (P2) strategy is receiving significant attention in industries all over the world, over end-of-pipe pollution control and management strategy. This paper is a review of the existing pollution prevention frameworks. The reviewed frameworks contributed significantly to bring the P2 approach into practice and gradually improved it towards a sustainable solution; nevertheless, some objectives are yet to be achieved. In this context, the paper has proposed a P2 framework 'IP2M' addressing the limitations for systematic implementation of the P2 program in industries at design as well as retrofit stages. The main features of the proposed framework are that, firstly, it has integrated cradle-to-gate life cycle assessment (LCA) tool with other adequate P2 opportunity analysis tools in P2 opportunity analysis phase and secondly, it has re-used the risk-based cradle-to-gate LCA during the environmental evaluation of different P2 options. Furthermore, in multi-objective optimization phase, it simultaneously considers the P2 options with available end-of-pipe control options in order to select the sustainable environmental management option.     

Design and realisation of an efficient environmental assessment method for 3R systems: a case study on engine remanufacturing

Recently, the manufacturing industry has been striving for sustainability because of the environmental degradation and resource depletion caused by it. Remanufacturing considerably saves material and is energy efficient, and thus, it can represent an important solution to environmental issues. However, the uncertainty of remanufacturing makes the practical management of closed-loop supply chains (CLSCs) difficult. To unlock the value potential of end-of-life (EOL) products, we studied a reuse, remanufacture, and recycle (3R) processing system under quality uncertainty for returned EOL engines. In the system, the returned cores were distributed into different processing routes, depending on the results of quality grading. The proposed matrix operations could efficiently assess the environmental benefits; moreover, we designed an algorithm to calculate the quality coefficient that reflects the overall quality condition of returned EOL cores. The impacts of quality uncertainty on the environment could be efficiently quantified via our proposed method. Furthermore, using Monte Carlo simulation and the law of large numbers, we devised a model to establish direct and definite quantitative relationships between the quality coefficient and production indexes. This model provides a basis for the formulation of optimal acquisition strategies under different returning scenarios.     

Robust multi-objective optimization for energy production scheduling in microgrids

In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.     

Synergetic Management Theory for Coal-Based Energy Engineering and the Engineering Practice of Shenhua

To deal with problems in synergetic development of coal-based energy engineering, this paper, guided by the philosophy of engineering, proposes the synergetic management philosophy of “factors coordination, systems synergy, dynamic optimization and three-dimension planning.” The paper also establishes the synergetic management system characterized by systems factor synergy, resource-environment synergy and systems boundary extension and supported by the “two-wheel driven” management innovation and technological innovation. In addition, the paper presents a multi-objective dynamic optimization model for energy engineering, designed based on Shenhua’s own engineering practice, to analyze Pareto optimal solution in three scenarios: best resource allocation, minimal environmental impact and maximal value creation. This provides important reference to synergetic development strategies and decision-making in engineering management.     

A framework for the design of knowledge management systems in eco-design

Eco-design is becoming a crucial challenge for many companies. The effective integration of environmental concerns into design is linked to the development of Knowledge Management Systems (KMS). In this context, this paper proposes a framework to support the design of KMS for eco-design. This framework consists of two complementary parts linked to the main knowledge management strategies that KMS generally support. The first part of the framework concerns codification strategy and relies on an eco-design process reference model and the related adaptation of the GAMETH^® method used to locate and evaluate crucial knowledge systematically. The second part of the framework concerns the personalisation strategy. In this paper we propose to exploit the community of practice (CoP) theory by: (1) proposing guidelines defining the structural features of CoPs and (2) adapting a tool landscape providing insight on CoP instrumentation. Both guidelines support the setting up of a specific CoP. The exploitation of the framework is illustrated on a real case study.     

Persistent Integration and Innovation Management Mode and Its Applications in Petroleum Production Engineering

This paper proposes a new management mode named the persistent integration and innovation management mode to satisfy the requirements by the fast development at oilfields. Taking engineering philosophy as the guidance and technical innovations in petroleum production engineering as the breaking point, the management mode combines long term production practice at oilfields with modern management methods. The management mode integrates intellectuals, techniques, capital, and information and so on to accomplish global optimization of resource configuration. Sets of new techniques, new methods, new technologies and new equipment in petroleum production engineering have been proposed under the guidance of the management mode, achieving great social and economic benefits.     

Sequential linear programming and particle swarm optimization for the optimization of energy districts

This article deals with the optimization of energy resource management of industrial districts, with the aim of minimizing customer energy expenses. A model of the district is employed, whose optimization gives rise to a nonlinear constrained optimization problem. Here the focus is on its numerical solution. Two different methods are considered: a sequential linear programming method and a particle swarm optimization method. Efficient implementations of both approaches are devised and the results of the tests performed on several energetic districts are reported, including a real case study.     

基于GA-ANFIS在石灰矿技术经济系统中的参数优化研究与应用实践

为掌握水泥原料矿山系统中的技术经济参数对矿石成本影响的关联规律性，首先运用自适应模糊神经网络对矿山技术经济系统建模，再用并行遗传算法对模型求解，得到了确保矿石成本最小的各项最优技术经济指标，为提高矿山生产管理与经济效益提供了重要的参考价值。     

Error and uncertainty in modeling and simulation

This article develops a general framework for identifying error and uncertainty in computational simulations that deal with the numerical solution of a set of partial differential equations (PDEs). A comprehensive, new view of the general phases of modeling and simulation is proposed, consisting of the following phases: conceptual modeling of the physical system, mathematical modeling of the conceptual model, discretization and algorithm selection for the mathematical model, computer programming of the discrete model, numerical solution of the computer program model, and representation of the numerical solution. Our view incorporates the modeling and simulation phases that are recognized in the systems engineering and operations research communities, but it adds phases that are specific to the numerical solution of PDEs. In each of these phases, general sources of uncertainty, both aleatory and epistemic, and error are identified. Our general framework is applicable to any numerical discretization procedure for solving ODEs or PDEs. To demonstrate this framework, we describe a system-level example: the flight of an unguided, rocket-boosted, aircraft-launched missile. This example is discussed in detail at each of the six phases of modeling and simulation. Two alternative models of the flight dynamics are considered, along with aleatory uncertainty of the initial mass of the missile and epistemic uncertainty in the thrust of the rocket motor. We also investigate the interaction of modeling uncertainties and numerical integration error in the solution of the ordinary differential equations for the flight dynamics.     

Home Region Focus and Technical Efficiency of Multinational Enterprise

• The transaction costs economics (TCE) perspective on regionalization suggests that multinational enterprises (MNEs) would experience advantages from regionalization and, hence, greater technical efficiency from a high home region focus (HRF). We extend this TCE perspective by proposing that whether a regional (i.e., higher HRF) or global (i.e., lower HRF) strategy leads to greater technical efficiency depends on the degree of regional integration (i.e., economic and policy) of the MNEs?? home regions.
• This is the first study in the regional/global strategies literature to analyze the effects of HRF and regional integration (economic and policy) on firms?? technical efficiency performance. We suggest that advantages from regionalization arise when firms align their HRF strategy with the degree of regional integration; disadvantages from regionalization can arise when the two are misaligned.
• Our empirical analysis on a sample of 645 manufacturing Triad MNEs during 2000?C2006 provides overall support for our conceptual framework.

     

Environmental and economic assessment of sugarcane first generation biorefineries in Brazil

The majority of current sugarcane mills in Brazil are annexed plants, producing both ethanol and sugar; the other plants are dedicated only to ethanol production (autonomous distilleries). This study focuses on the technical, environmental, and economic impacts of these first generation sugarcane processing facilities in Brazil using an innovative framework, the so called Virtual Sugarcane Biorefinery. Results showed that optimization technologies presents potential for reducing environmental impacts and improve economic results in comparison to base scenarios for both annexed and autonomous plants. It was also observed that annexed plants that diverted more sugarcane juice for sugar production were more profitable, considering the average prices for the past 10?years in Brazil. In addition, results indicate that scenarios considering more flexibility in annexed plant are more profitable than the conventional annexed one (diverting 50% of the sugarcane juice to sugar and 50% to ethanol production) if increases in prices were to occur. This study shows quantitatively the benefits of optimization techniques and allows understanding the real benefits of the sugarcane plant flexibility in the Brazilian context.     

Data analytics and optimization for smart industry

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization tech-nologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.     

经济全球化环境下泛珠三角区域物流一体化动因及其模式研究

泛珠三角经济合作对区域物流一体化提出了迫切要求。区域经济一体化造就了区域物流一体化，而经济互补、社会分工、先进制造模式和管理理念、以及信息网络技术的发展成为推动泛珠三角区域物流一体化的外部重要动力，同时泛珠三角区域物流系统因为追求物流资源优化、区域物流费用最小化以及区域物流产业自身发展等目标也极大地促进了泛珠三角区域物流一体化进程。在该过程中，“政府驱动＋市场化整合”将成为泛珠三角区域物流一体化的主要模式。