A multidisciplinary decision support tool for evaluating multiple biorefinery conversion technologies and supply chain performance

Fuels from biomass resources have emerged as a promising alternative to fossil fuel. Widely distributed source locations, varying feedstock, and specific regional conditions make it challenging to develop an optimization model that can be applied to any region to estimate the overall economics of producing these biofuels. The lack of a region specific, flexible optimization model makes it difficult for stakeholders like local policy makers, growers, and investors to make informed decisions about the economic viability and social and environmental impacts of biomass utilization. This novel contribution will illustrate an approach to develop a region specific optimization model which links various aspects of the biofuel supply chain such as feedstock source location, upstream and downstream logistics, as well as thermochemical and biochemical processing. This research shows how various individual optimization models can be combined, resulting in a complete, multivariable economic optimization model for a regional biomass network, paving a pathway for future work to develop an integrated framework for sustainability. The research demonstrated in this contribution illustrates the development of a model that can form the basis of a generalizable decision support tool that can guide investors and policy makers in making critical assessments on a local level in any particular region of interest. As a proof of concept, a portion of the described model will be validated using the Jackson Purchase region of Western Kentucky, USA, which is adjoining many coal fields and has various bio-based feedstocks.     

Production scheduling of a lignite mine under quality and reserves uncertainty

The effect of uncertainty sources to the stochastic optimization of the combined project of a new surface lignite mine exploitation and power plant operation for electricity generation is investigated. Major sources of uncertainty that were considered are the reserves and the quality of the lignite. Since probability distribution functions for these uncertainties were estimated during the detailed exploration phase of the deposit, the overall goal is then to determine the optimal capacity of the power plant and consequently the optimal production rate of the mine over the time. The optimization objective that was selected is the maximization of the net present value of the project. Emphasis is placed on the sensitivity analysis for the investigation of the effect of quality and reserves uncertainty on project optimization, on the mathematical formulation of risk attitude strategy and on increasing the efficiency of the optimization process by creating a limited set of feasible solutions applying empirical rules. The developed methodology was applied for the determination of the optimal annual production rate of a new surface lignite mine in the area of Ptolemais–Amynteon in Northern Greece.     

Negotiating price/delivery date in a stochastic manufacturing environment

We study a make-to-order manufacturing system consisting of several processing centers that are subject to failures and repairs. Our objective is to build a model that can be used as a tool for negotiating the delivery date and the price of a certain upcoming order. The model takes into account the congestion level of the shop floor at the time the order is placed. Based on the workload of the processing centers, the model splits the order into lots and assigns them to the processing centers so as to determine the order completion time associated with the minimum operating cost. The efficiency of the solution method for the model allows real-time decision-making while negotiating the price and delivery date of the order to be placed. Since the decisions are made based on a snapshot of the congestion level at the shop floor, using this model will reduce the conflict between the marketing and the production activities in manufacturing organizations.     

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.     

Iogen's process for producing ethanol from cellulosic biomass

The production of fuel alcohol from cellulosic biomass is of growing interest around the world. Cellulosic biomass can be used to produce transportation fuel, with the overall process having little net production of greenhouse gases. Biomass is available as a byproduct of many industrial processes and agricultural materials, or can be potentially produced from dedicated energy crops. The technology for biomass conversion has many significant technical and economic hurdles that have prevented commercialization to this point. However, significant progress in recent research has motivated Iogen Corporation to build a 40 t/day plant in Ottawa, Canada to demonstrate the technology. Electronic Publication     

A generalized multi-pass machining model for machining parameter selection in turning

A machining parameter selection model has been developed using multi-pass turning operation as a general type. An approximation optimization solution approach has been defined for this machining model. This model is a constrained model with the objective of achieving minimum cost. Depth-of-cut and number-of-pass are considered as variables in the model. All parameters, including the number-of-passes, cutting speed, feed rate and depth-of-cut for each pass, reach optimal simultaneously in one stage. The performance of this machining parameter optimization model is examined using a example, and the results are compared with current publications that provide same input data.     

OPTIMIZATION OF NONWOVEN FILTER PERFORMANCE FOR INTERMEDIATE REYNOLDS NUMBERS

The selection of a nonwoven air filter structure that is optimal for specific conditions is not simple and is affected by many factors. Traditionally, the selection process is based on experience and professional instinct. However, this approach to decision-making lacks an objective basis for making comparisons between alternative structures. In order to optimize the effectiveness of the filter performance, the decision-making process must be placed on a rational and objective basis.

In this paper, a general approach to a multicriteria optimization is discussed. Linear and nonlinear models are analyzed as well as compound criteria obtained by various combinations of simple criteria. An essential component of the optimization model is the criterion function, which is a mathematical expression of the optimization criterion deduced from the goals. Each criterion represents a filter characteristic, such as filter efficiency, pressure drop, flow rate, dust-holding capacity and, in some cases, economic indices.

The formulation of the optimization problem involves transforming filter performance into an equivalent mathematical model. A set of equations was developed to describe initial efficiency and pressure drop as well as efficiency and pressure drop for dust loaded nonwoven media at intermediate Reynolds numbers.

Using a simple mathematical method, the optimal structure of a nonwoven filter for a given set of conditions was obtained. A compound criterion can also be used as a quality factor in order to make comparisons between filters of different structures.     

Optimization of bioethanol and sugar supply chain network: a South African case study

This paper presents an economic optimization of a supply chain network to produce sugar and bioethanol from the first- and second-generation biomass in South Africa. A mixed-integer linear program model in combination with Google Earth^® and ArcGIS was hereby developed to account for sugar demand, different feedstocks and products, as well as their distribution, tax subsidies and different processing technologies to address bioethanol production. The model was applied to two case studies depending on availability of biomass of which the results showed that the integrated bioethanol and sugar supply chain network is economically viable in South Africa. Transportation costs accounted for a significant part of the costs incurred in the supply chain, while highest profits are made when higher amounts of raw materials are available and there is no restriction relating to ethanol production. It is concluded that the 2% bioethanol-fuel blend target stipulated by the government of South Africa in the biofuel industrial strategy can be exceeded while still producing current amounts of sugar.     

Derived heuristics-based consistent optimization of material flow in a gold processing plant

Material flow in a chemical processing plant often follows complicated control laws and involves plant capacity constraints. Importantly, the process involves discrete scenarios which when modelled in a programming format involves if–then–else statements. Therefore, a formulation of an optimization problem of such processes becomes complicated with nonlinear and non-differentiable objective and constraint functions. In handling such problems using classical point-based approaches, users often have to resort to modifications and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, these facts are demonstrated by showing results from MATLAB®'s well-known fmincon routine. Thereafter, a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem is developed. Although the evolutionary approach produced results with comparatively less variance over multiple runs, the performance has been enhanced by introducing derived heuristics associated with the problem. In this article, the development and usage of derived heuristics in a practical problem are presented and their importance in a quick convergence of the overall algorithm is demonstrated.     

Techno-economic evaluation of PHB production from activated sludge

Activated sludge biomass enriched under suitable conditions is a promising source for poly-3-hydroxybutyrate (PHB) production, a biodegradable polymer. The techno-economic evaluation of PHB production using activated sludge biomass has been carried out. The PHB yield and plant capacity was found to significantly affect the PHB production cost. The PHB production cost for a plant processing (fermentation) capacity of 100 m³/day and 44% PHB yield is about US$ 11.8/kg, which reduces to US$ 5.38/kg for 1,000 m³/day plant capacity and 70% PHB yield. The cost of the carbon substrate was found to significantly affect the overall economics of PHB production.     

应用遗传算法和神经网络的工程电梯传动机构模糊优化设计

 考虑到影响设计的某些因素很难用确定数值表示,以工程电梯传动机构中蜗轮齿冠体积最小为优化目标,建立了模糊优化设计的数学模型。采用二级模糊综合评判法按最大隶属度原则求出最优水平截集,将模糊优化问题转化为普通优化问题。另外,通过神经网络方法得出网络权值和阈值以拟合待求系数,并采用加法形式的惩罚策略来构造带有惩罚项的适值函数,应用Matlab遗传算法工具箱寻求问题最优解,从而提高设计精度和搜索效率。     

基于分布式光伏发电的多目标分时电价优化策略

现有电力定价研究大多为峰谷分时定价,时段划分方式单一且大多采用传统非支配排序遗传算法-II求解多目标问题。针对这个问题,提出一种基于分布式光伏发电的多目标分时电价优化策略。建立用电量与电价响应模型,基于等效负荷进行时段划分,以负荷方差最小,等效负荷的峰谷差最小,用户满意度指数最大为目标,建立多目标非线性分布式光伏分时定价模型,并提出基于邻域搜索的多目标遗传算法求解。数值实验结果表明,供电稳定性提高了37.77%,分布式光伏发电的利用率提高了4.51%,用户满意度为74.3%;且提出的求解算法要优于常用的非支配排序遗传算法-II,表明本文提出的定价策略是有效的。     

Optimal scheduling of biocide dosing for seawater-cooled power and desalination plants

Thermal desalination systems are typically integrated with power plants to exploit the excess heat resulting from the power-generation units. Using seawater in cooling the power plant and the desalination system is a common practice in many parts of the world where there is a shortage of freshwater. Biofouling is one of the major problems associated with the usage of seawater in cooling systems. Because of the dynamic variation in the power and water demands as well as the changes in the characteristics of seawater and the process, there is a need to develop an optimal policy for scheduling biocide usage and cleaning maintenance of the heat exchangers. The objective of this article is to introduce a systematic procedure for the optimization of scheduling the dosing of biocide and dechlorination chemicals as well as cleaning maintenance for a power production/thermal desalination plant. A multi-period optimization formulation is developed and solved to determine: the optimal levels of dosing and dechlorination chemicals; the timing of maintenance to clean the heat-exchange surfaces; and the dynamic dependence of the biofilm growth on the applied doses, the seawater-biocide chemistry, the process conditions, and seawater characteristics for each time period. The technical, economic, and environmental considerations of the system are accounted for. A case study is solved to elucidate the applicability of the developed optimization approach.     

Techno-economic analysis of organosolv pretreatment process from lignocellulosic biomass

Lignocellulosic ethanol is a promising alternative to replace liquid fossil fuels for the transportation sector in the near future. Organosolv pretreatment has been tested as a method for separating lignin from the biomass and commercializing it as a biopolymer. Based on published laboratory scale data, we propose a feasible process flowsheet for organosolv pretreatment. Simulation of the pretreatment process provided mass and energy balances for a techno-economic analysis, and the values were compared with the most prevalent and mature pretreatment method: diluted acid. Organosolv pretreatment required more energy, 578.1 versus 213.8 MW for diluted acid pretreatment, but resulted in a higher ethanol concentration after the biomass fermentation, 11.1% compared to 5.4%. Total annual costs (TACs) calculations showed advantages for diluted acid pretreatment, but future improvements explored in the sensitivity analysis turned into possible savings of 42.8% in the minimum ethanol selling price for organosolv pretreatment.     

Profit rate performance optimization for a generalized irreversible combined refrigeration cycle

Finite-time exergoeconomic performance of a Newtonian heat transfer law system generalized irreversible combined refrigeration cycle model with finite-rate heat transfer, heat leakage and internal irreversibility is presented in this paper. The operation of the generalized irreversible combined refrigeration cycle is viewed as a production process with exergy as its output. The performance optimization of the cycle is performed by taking profit as the objective. The optimal profit rate, optimal COP (coefficient of performance), as well as the relation between the optimal profit rate and COP of the cycle are derived. The focus of this paper is to obtain the compromise optimization between economics (profit rate) and the energy utilization factor (COP) for the cycle, by searching the optimum COP at maximum profit rate, which is termed as the finite time exergoeconomic performance bound. Moreover, the effects of various factors, including heat leakage, internal irreversibility and the price ratio, on the profit rate performance of the cycle are analysed by detailed numerical examples.     

基于粒子群算法的空间直线度误差评定

提出了一种满足最小区域法的空间直线度误差评价的新方法--粒子群算法。根据最小区域条件,建立了空间直线的数学模型以及优化目标函数。阐述了粒子群优化算法的原理和实现方法,然后根据粒子群算法优化求解。实例表明该方法对于空间直线度误差评定等非线性优化问题能得到最优解,可用于三坐标测量机等测量系统的空间直线度误差测量的数据处理。     

客户偏好约束下的网络零售配送时隙分配优化

在B2C环境下,提高配送时隙运营效率,降低运营成本,尽力通过高效的资源利用率来降低成本,从而产生良好的经济效益,成为网络零售商提高竞争力的关键所在。本文规避单目标研究不能够较为全面反映配送的实际问题,拟考虑从多目标角度对问题进行优化,建立了基于客户偏好的时隙偏差最小和时隙配送成本最小的综合优化模型。最后通过算例分析,模拟几种不同情况,综合优化模型在时间和成本水平下都较传统单目标更优,能使配送时隙综合效用最大。     

考虑时间可靠度约束的4PL路径优化问题研究

运输时间的不确定性是影响第四方物流路径选择的重要因素。本文 将可靠性理论应用到路径优化问题中,把客户对时间可靠度的要求作为约束条件,以运输总费用最小为目标,基于多重图建立考虑时间可靠度约束的第四方物流路径优化问题的机会约束规划模型,设计双列变长编码机制的遗传算法求解模型。通过仿真实例验证模型和算法的有效性,结果表明针对客户对时间可靠度要求的不同,该算法可以快速并有效地帮助决策者做出最优的路径选择。     

Topology optimization of multi-material for the heat conduction problem based on the level set method

This article presents a numerical approach of topology optimization with multiple materials for the heat conduction problem. The multiphase level set model is used to implicitly describe the geometric boundaries of material regions with different conductivities. The model of multi-material representation has no emergence of the intermediate density. The optimization objective is to construct the optimal heat conductive paths which improve the efficiency of heat transfer. The dissipation of thermal transport potential capacity is taken as the objective function. The sensitivity analysis is implemented by the adjoint variable method, which is the foundation of constructing the velocity field of the level set equation. The optimal result is gradually realized by the evolution of multi-material boundaries, and the topological changes are naturally handled during the optimization process. Finally, the numerical examples are presented to demonstrate the feasibility and validity of the proposed method for topology optimization of the heat conduction problem.     

Capacity requirements planning—optimal work station capacities

The How of work through a work station is analysed in terms of planned input from previous stations, planned output to subsequent stations, planned work-in-process waiting for processing and the planned queue of unprocessed work delayed for processing during sonic subsequent period. The planner is assumed to have some leeway in determining the maximum throughput during each period7mdash; the planned capacity—and is thus interested in determining the optimal capacity, to be maintained at some constant value throughout the planning horizon. This paper presents a methodology for finding the optimal planned capacity of a work station, which minimizes total costs over the planning horizon. These costs are of three types: costs of establishing and maintaining the capacity level; costs of work processing; and costs of hoi ding up work in the queue owing to insufficient capacity at any period.

Various constraints are considered when characterizing the operating environment of the work station: minimum and maximum permissible queue lengths, delays, work-in-process amounts, lead times, underloading and backlogging. It is shown that these restrictions may be transformed into minimum and maximum bounds on allowable capacity levels, so that the problem becomes one of constrained optimization in a single decision variable—the planned capacity. For complex cost functions or when underloading is permissible, complete enumeration of all feasible solutions provides the simplest approach. For linear or quadratic cost functions and no underloading, the shape and smoothness of the total cost curve indicates that set of feasible capacities within which the optimal capacity lies.