石化项目建设期安全生产费用管理要点

石化项目安全生产费用管理贯穿项目的整个生命期,本文主要阐述建设期安全生产费用管理。按照初步设计阶段、施工图设计阶段、招投标阶段、执行阶段的工程进展顺序论述了相关费用管理政策文件及标准的要求。随后从具体执行的过程中描述文件精神的执行及应注意的问题。通过本文的论述,使石化项目费用管理者明白自己可以依据那些政策性有重点的参与安全生产。     

太阳能硅晶片生产行业环保验收监测——以江西某半导体新材料有限公司多晶硅项目一期工程为例

本文依据项目竣工环保验收规范性文件,具体介绍了太阳能硅晶片的生产行业相关环保设施,论述了此类项目环保验收监测工作中应关注的重点问题。结合具体的工程实例对目前存在的问题及解决方案进行了详细说明。     

MBT树脂实现资源化利用

日前,MBT树脂资源化利用及万吨级复合橡胶助剂开发项目在北京通过了中国石油和化学工业联合会组织的技术鉴定。该技术解决了橡胶促进剂MBT生产过程中的中间产物MBT树脂难以资源化利用的问题,填补了国内空白。该项目由山东尚舜化工有限公司和青岛科技大学共同完成。鉴定专家委员会认为,该项目属国内领先水平,建议其进一步扩大生产规模和应用领域。该项目主要创新点有3个。     

PVC生产中艾默生DCS控制系统的升级改造

总结了艾默生DCS控制系统在PVC生产运行及项目扩建中出现的各类问题,并制定了相应的系统优化升级方案,提升了DCS系统的性能,保证了PVC的安全稳定生产。     

农药类项目环境影响评价问题探讨

近年来,国家对新建农药类项目十分重视,加大了生产管理力度,该类项目的环评报告书需报圄家环保总局审批。笔者结合对多项农药类项目环评工作实际,针对农药类项目建设及环评中存在的主要问题,对农药类项目环评所应依据的主要法律法规、产业政策以及进行环境现状调查、工程分析、影响预测、风险评估等应重点关注的问题进行了阐述与分析。     

基于PDCA循环的新时期煤化工安全生产管理探究

阐述了新时期煤化工行业安全生产管理的必要性,介绍了基于PDCA循环的新时期煤化工安全生产管理运作模式,并对新时期我国煤化工项目安全生产中运用PDCA循环理论及应注意的问题作了具体分析。     

化工项目设计过程中的安全生产技术

对于现在的化工项目来说,其施工和生产过程中的安全隐患排查和安全控制是一个重要的环节。在现在的化工项目中,不可避免地存在着许多安全隐患,因此,就需要对化工项目的施工和投产之前进行正规的、符合法律法规及相关规范的设计,为解决施工和生产过程中的问题提出更好的安全控制方案,减少化工项目的安全隐患。因此,通过对化工项目中存在的安全问题进行分析,提出问题处理的对策。     

化工企业安全评价方法研究

对于化工企业而言,其生产过程通常危险性较大,因而安全问题一直以来都是化工企业生产管理过程中关注的重点。为了有效防范及解决化工企业生产过程中的安全性方面的问题,在化工企业中应用安全评价方法是一种十分重要的技术手段。本文就化工企业中的安全评价原则、方法进行研究,以实例为依据进行了分析,对于化工项目的生产及实施具有较好的指导及参考意义。     

浅谈泡沫玻璃在LNG项目中的应用

本文总结归纳了泡沫玻璃的发展简史,对其生产所使用的原材料及辅料的情况进行概括,对国内外泡沫玻璃的生产工艺进行对比,对各流派的生产技术进行描述,概括生产设备的发展,并对生产过程中可能出现的质量问题提出解决方案;再对LNG项目的特性进行阐述,结合LNG管道及仓储设备的保冷性能要求,对泡沫玻璃应用在LNG项目中的优势做出论证。     

建筑工程项目施工质量控制系统的实现途径

建筑工程项目施工质量对人们的正常生活及生产有十分重要的作用,只有确保建筑工程项目施工质量符合相关规定,才能促进和谐社会的构建,因此,在进行建筑工程项目施工时,加强施工质量控制具有十分重要的意义。文章从建筑工程项目施工质量控制存在的问题出发,分析了影响建筑工程项目施工质量的因素,并提出了建筑工程项目施工质量控制系统的构建措施。     

A MILP model for N-dimensional allocation

This paper presents a Mixed Integer Linear Programming (MILP) model for the solution of N-dimensional allocation problems. The applicability of the model is presented and demonstrated through some illustrative examples with different numbers of dimensions. Several problems, previously presented in the literature, are solved using the proposed model, such as, one-dimensional scheduling problems, two-dimensional cutting problems, as well as plant layout problems and three-dimensional packing problems. Additionally, some problems in four dimensions are presented and solved using the considered model. The presented model is applicable to a wide variety of allocation problems as it offers a general framework for modelling allocation problems with any given number of continuous or discrete dimensions. The presented problems are formulated as MILP problems where the first four dimensions usually are continuous spatial and time dimensions. Additional dimensions are often of a discrete nature.     

A generalization of the Branch-and-Sandwich algorithm: From continuous to mixed-integer nonlinear bilevel problems

We propose a deterministic global optimization algorithm for mixed-integer nonlinear bilevel problems (MINBP) by generalizing the Branch-and-Sandwich algorithm (Kleniati and Adjiman, 2014a). Advances include the removal of regularity assumptions and the extension of the algorithm to mixed-integer problems. The proposed algorithm can solve very general MINBP problems to global optimality, including problems with inner equality constraints that depend on the inner and outer variables. Inner lower and inner upper bounding problems are constructed to bound the inner optimal value function and provide constant-bound cuts for the outer bounding problems. To remove the need for regularity, we introduce a robust counterpart approach for the inner upper bounding problem. Branching is allowed on all variables without distinction by keeping track of refined partitions of the inner space for every refined subdomain of the outer space. Finite ɛ-convergence to the global solution is proved. The algorithm is applied successfully to 10 mixed-integer literature problems.     

PARALLEL SOLUTION OF DISJUNCTIVE MINLP PROBLEMS

In this paper, a system for parallel solution of large scale disjunctive optimization problems is presented. Disjunctive optimization problems occur in process engineering; both in production scheduling and in process synthesis. In general these problems are computationally very hard to solve.

This system combines the demands of process engineering optimization problems with the potential CPU-power of the computers connected to the Internet. The system is capable of using an arbitrary number of computers connected to the Internet for distributed solution of rigorous industrial optimization problems.     

A global optimization algorithm (GOP) for certain classes of nonconvex NLPs—II. Application of theory and test problems

In Part I (Floudas and Visweswaran, Computers chem. Engng 14, 1397, 1990), a deterministic global optimization approach was proposed for solving certain classes of nonconvex optimization problems. An algorithm, GOP, was presented for the rigorous solution of the problem through a series of primal and relaxed dual problems until the upper and lower bounds from these problems converged to an -global optimum. In this paper, theoretical results are presented for several classes of mathematical programming problems that include: (i) the general quadratic programming problem; (ii) quadratic programming problems with quadratic constraints; (iii) pooling and blending problems; and (iv) unconstrained and constrained optimization problems with polynomial terms in the objective function and/or constraints. For each class, a few examples are presented illustrating the approach.     

Optimizing Injection Molding Toward Multiple Quality and Cost Issues

Injection-molding part designers are frequently faced with multiple quality and cost issues. These issues are usually in conflict with each other; thus, a trade-off needs to be made to reach a final compromised solution. Because evaluation of part quality and cost via injection-molding simulation is very time-consuming, implementation of a conventional multicriteria optimization procedure for injection-molding problems is economically unfavorable. However, many injection-molding problems dealing with multiple quality and cost issues can be modeled as constrained problems, with the total cost as the objective function and quality quantities as the constraints. By introducing a concept of penalized total cost, such constrained problems are further simplified into bounded single-criterion problems. The bounded single-criterion problems are then optimized using a direct search-based optimization procedure. Strategies of modeling, transformation, and optimization for these problems are discussed in this article. A case study is provided.     

Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design

Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity.     

天然气蒸汽转化过程的节能途径分析

介绍了大型合成氨工艺中，天然气蒸汽转化过程的九种节能途径，详述其原理，对采用此途径所出现的新问题作了剖析，提出了解决措施，并列举国内外的实例加以说明。     

环境统计工作存在的问题及解决方法

我国自1983年开展环境统计工作以来,环境统计范围不断扩展,环境统计制度不断完善,环境统计数据在综合反映环境状况、服务环境管理和科学决策方面起到了重要的基础性作用。随着我国经济的快速发展,环境问题越来越受到重视,对环境统计数据的需求越来越多,对环境统计在全面性、及时性、准确性以及综合分析等方面提出更高的要求。现行的环境统计已不适应形势发展的需要,必须进行改革。要加强环境统计工作改革步伐,简化指标,提高效率,要及时、准确地提供统计数据,客观地反映经济发展与环境形势的变化,真正为环境管理和决策服务。     

An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units

The demand for fast solution of nonlinear optimization problems, coupled with the emergence of new concurrent computing architectures, drives the need for parallel algorithms to solve challenging nonlinear programming (NLP) problems. In this paper, we propose an augmented Lagrangian interior-point approach for general NLP problems that solves in parallel on a Graphics processing unit (GPU). The algorithm is iterative at three levels. The first level replaces the original problem by a sequence of bound-constrained optimization problems using an augmented Lagrangian method. Each of these bound-constrained problems is solved using a nonlinear interior-point method. Inside the interior-point method, the barrier sub-problems are solved using a variation of Newton's method, where the linear system is solved using a preconditioned conjugate gradient (PCG) method, which is implemented efficiently on a GPU in parallel. This algorithm shows an order of magnitude speedup on several test problems from the COPS test set.     

APPLICATION OF THE TRANSFORM-ITERATIVE METHOD TO NONLINEAR CONCENTRATION BOUNDARY VALUE PROBLEMS

In this paper we present a “somewhat” new method that treats nonlinear concentration related boundary value problems, among other problems. The method uses a general convolution product to transform the nonlinear term, then it iterates with successive division inside the integral to arrive at the approximate solution. It will also include an explanation to the apparent success of what was recently termed “novel transform method” for such nonlinear problems, as being a very special case. As such, it represents the first mathematical basis of any kind, that would offer a reason for the limited success of the recent “novel” method. The present method is illustrated here, for a porous catalyst slab with an outline for other geometries and different non-linearities. The numerical results agree very well with the known exact results of these problems, and other problems in different fields.