复合推进剂成型与发动机总装集成工艺技术创新

分析了大型发动机连续装药工艺,采用大型立式混合机混合,设计了连续注入(螺杆)挤压注射浇灌工艺技术试验装置。为解决国外SME(塑型"高聚物黏结炸药")工业化存在的问题(配方组分化合物进给、固体粉料(高氯酸铵)干摩擦的安全性等),提出了建设性见解;对输送推进剂药浆并排除气体而不产生烟火风险,在螺杆连续出料口直接浇注推进剂药柱技术问题等,提出了创新性设计思路论证和实施方法。研讨了大型发动机连续浇注成型/总装工程化设计思路。     

某弹射弹推进剂装药设计

通过对某弹射弹功能和使用要求分析，确定了该弹射弹的极限特征参数及弹道性能，提出了装药用推进剂的选取原则，经过弹道计算和分析，初步确定了所用推进剂及装药的药型，并进行了试验验证，为该弹射弹的研制打下了良好的基础。     

减少高燃速推进剂药管内外压差的优化设计方法

通过分析高燃速推进剂燃烧室内流场结构，讨论了在工作时间，装药量，发动机长度不变的条件下，装药结构优化设计方法，使药管内外压差值达到最小，避免发动机非正常工作状况。     

高能复合推进剂用于反应式装甲装药的探讨

叙述了含端羟基聚合物的固体复合推进剂在反应式装甲上的应用，并对该装药的应用前景进行了探讨。     

化工过程集成设计研究进展

化工过程设计中最重要的两个方面是单元设备与工艺流程的设计以及配套控制系统的设计,化工设备作为化工生产的硬件,控制系统作为驱动硬件设备生产的软件,为了更好地利用"软件"控制系统驱动"硬件"化工设备进行生产,化工过程控制与工艺的集成设计是大势所趋,因此对公开文献中的数学模型进行了详细综述和对比。     

粒铸无烟推进剂研究

介绍了国内一种新型的固化推进剂和它的制造工艺－造粒浇铸工艺及用该工艺制造的无烟推进的主要性能，为国内固体推进剂增添了一个新的品种。     

CC300：集成工艺的智能化管理

工艺的集成与自动化对于提高注塑成型的生产效率至关重要。然而,它们同时也使制造过程变得越来越复杂。     

环氧树脂浇注及浇注工艺

环氧树脂浇注是将环氧树脂、固化剂和其他配合料浇注到设定的模具内，由热塑性流体交联固化成热卧性制品的过程。由于环氧树脂浇注产品集优良的电性能和力学性能于一体，因此环氧树脂浇注在电器工业中得到了广泛的应用和快速的发展。     

基于建筑智能化系统工程项目集成管理的分析

基于社会经济的不断发展和科学技术的不断提高,建筑智能化趋势也越来越明显。而在建筑智能化系统工程项目中,集成管理则是其实现有效运作的关键。因此做好集成管理工作,才能实现智能大厦的安全性和效益性。本文将通过对建筑智能化系统工程项目集成管理的重要性、设计和要点三个方面进行分析,以希望不断提高搞项目集成管理的水平。     

压铸件浇注系统切断模设计

主要介绍了去浇注系统模具的设计理念，根据以往出现的问题，采取了相应地解决办法。     

System design principles for ultrasonic casting plant for a highly refractory oxide ceramic

Methodological principles are formulated for selecting the scheme and the design parameters for the acoustic system for ultrasonic casting of a highly refractory ceramic. Relationships are established for optimizing the position of the waveguide in relation to the parameters of the acoustic processing and the volume of the simultaneously processed material. The results have been used in setting up an ultrasonic extrusion system. Translated from Novye Ogneupory, No. 11, pp. 54–57, November, 2008.     

Simultaneous mixed-integer dynamic optimization for integrated design and control

We consider strategies for integrated design and control through the robust and efficient solution of a mixed-integer dynamic optimization (MIDO) problem. The algorithm is based on the transformation of the MIDO problem into a mixed-integer nonlinear programming (MINLP) program. In this approach, both the manipulated and controlled variables are discretized using a simultaneous dynamic optimization approach. We also develop three MINLP formulations based on a nonconvex formulation, the conventional Big-M formulation and generalized disjunctive programming (GDP). In addition, we compare the outer approximation and NLP branch and bound algorithms on these formulations. This problem is applied to a system of two series connected continuous stirred tank reactors where a first-order reaction takes place. Our results demonstrate that the simultaneous MIDO approach is able to efficiently address the solution of the integrated design and control problem in a systematic way.     

城镇给水厂集约化设计实例

基于全流程、全立体、全厂界的给水厂集约化理念,针对城镇中心城区超大型水厂原址的升级改造工程,采取了高效工艺优化、功能的拓展集成、紧凑的总图布置、空间的三维利用等方法,各个单元之间组合出叠合式、组团式、合建式等综合池型,有效提高了土地利用率,万吨水占地面积指标为1 600 m2。建成运行后,出厂水水质指标达到GB 5749—2006《生活饮用水卫生标准》,其中浊度≤0.1 NTU、色度≤5倍、ρ（CODMn）≤1.5mg/L、ρ（NH3-N）≤0.3 mg/L、ρ（Mn）≤0.05 mg/L。     

微生物细胞工厂的智能设计进展

在细胞工厂构建中设计-构建-测试-学习（design-build-test-learn,DBTL）循环是开发微生物细胞工厂的基本研究思路,其中设计环节尤为重要,然而传统的微生物细胞工厂设计方法主要依靠经验、费时费力、准确率低,影响了微生物细胞工厂的开发效率。当前,规模越发庞大的生物数据库和人工智能技术推动了微生物细胞工厂智能设计的快速发展,提升了在生物合成途径设计、调控元件设计和全局优化设计等方面的设计效率与应用。本文综述了微生物细胞工厂中途径预测、元件设计和途径与元件的组合三个环节中的智能设计工具,微生物细胞工厂智能设计的飞速发展将对生物制造领域产生变革性的影响。     

A multiobjective optimization framework for design of integrated biorefineries under uncertainty

A systematic approach for development of a reliable optimization framework to address the optimal design of integrated biorefineries in the face of uncertainty is presented. In the current formulation, a distributed strategy which is composed of different layers including strategic optimization, risk management, detailed mechanistic modeling, and operational level optimization is applied. In the strategic model, a multiobjective stochastic optimization approach is utilized to incorporate the tradeoffs between the cost and the financial risk. Then, Aspen Plus models are built to provide detailed simulation of biorefineries. In the final layer, an evolutionary algorithm is employed to optimize the operating condition. To demonstrate the effectiveness of the framework, a hypothetical case study referring to a multiproduct lignocellulosic biorefinery is utilized. The numerical results reveal the efficacy of the proposed approach; it provides decision makers with a quantitative analysis to determine the optimum capacity plan and operating conditions of the biorefinery. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3208–3222, 2015     

Global superstructure optimization for the design of integrated process water networks

We propose a general superstructure and a model for the global optimization for integrated process water networks. The superstructure consists of multiple sources of water, water‐using processes, wastewater treatment, and pre‐treatment operations. Unique features are that all feasible interconnections are considered between them and multiple sources of water can be used. The proposed model is formulated as a nonlinear programing (NLP) and as a mixed integer nonlinear programing (MINLP) problem for the case when 0–1 variables are included for the cost of piping and to establish optimal trade‐offs between cost and network complexity. To effectively solve the NLP and MINLP models to global optimality we propose tight bounds on the variables, which are expressed as general equations. We also incorporate the cut proposed by Karuppiah and Grossmann to significantly improve the strength of the lower bound for the global optimum. The proposed model is tested on several examples. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.