Carbon disulfide at a Chinese viscose factory external and internal exposure assessment

This article presents the results of carbon disulfide exposure measurements in a Chinese viscose rayon factory. The objectives of the study were to identify the external exposure levels at a large factory and to investigate the 2-thiothiazolidine-4-carboxylic acid (TTCA) concentrations in the urine of the subjects who were exposed to carbon disulfide in the working place atmosphere. The metabolism of carbon disulfide in the exposed subjects was also studied in order to demonstrate the best points in time for the internal exposure sampling. The measurement of the amount of personal exposure to carbon disulfide in the air of the workplace was performed by GC-FPD; the presence of TTCA in the workers urine was analyzed by use of a modified HPLC method. The kinetics of TTCA excretion was studied by analyses at different time-points both during and after exposure to carbon disulfide in the subjects. A total of 155 personal samples were obtained. The carbon disulfide concentration in the staple viscose hall was 13.72 +/- 1.12 mg m-3 in terms of the geometric mean +/- geometric standard deviation, and was 20.05 +/- 1.33 mg m-3 in the filament spinning hall. The TTCA values in the subjects who worked in the staple spinning hall were 1.18 +/- 0.43 mg g-1 creatinine and 1.07 +/- 0.38 mg g-1 creatinine for subjects working in the filament spinning hall. The best time for TTCA sampling is at the end of the working shift, the TTCA excretion was stable for a period of 4-12 h after exposure of the subjects to the carbon disulfide. It might be that the Chinese have different anthropometric characteristics; a sampling bias may therefore appear among different races.     

Water Network Synthesis Using Mutation-Enhanced Particle Swarm Optimization

Different techniques for the synthesis of industrial water reuse/recycle networks have been developed in recent process integration research. These tools range from graphical pinch analysis approaches to mathematical programming models. The latter have the advantage of being flexible enough to incorporate various water network constraints, but in many cases these are often non-linear, thus making the identification of global optima difficult. Recent work has demonstrated the effectiveness of metaheuristic algorithms such as particle swarm optimization (PSO), for finding good solutions these problems. This work describes the use of a modified PSO for solving mixed integer non-linear programming (MINLP) models for water network synthesis. By incorporating a mutation operator for the binary variables in the model, the algorithm is able to escape sub-optimal network topologies and proceed towards better solutions than can be found with ordinary PSO. Two case studies involving water recycle/reuse are used to demonstrate the new design methodology.     

DYNAMIC PROGRAMMING FOR OPTIMAL CAPACITY EXPANSION OF WASTEWATER TREATMENT PLANTS1

ABSTRACT: A dynamic programming procedure for the planning and operation of a wastewater treatment plant over a long period of time is presented. In order to meet increased demands for wastewater treatment in the future, the expansion of existing plants must be considered. Dynamic programming is employed to determine the optimal schedule of expansion at each plant, simultaneously determining an optimal operating policy (treatment level). The optimal schedule of expansion at each plant depends on the following: (1) the shape of the projected wastewater demand function; (2) the interest rate used; (3) the locations and capacities of the facilities available; and (4) the rates of increase of the costs of construction, labor, chemicals, and electric power. An example illustrating the use of the procedure is presented.     

USE OF A PREDICTIVE MODEL IN EVALUATING WATER CONSUMPTION CONSERVATION1

ABSTRACT: The effectiveness of water conservation policies on reduction of water consumption was evaluated by use of a multiple regression predictive model. Data were obtained from eight Iowa communities, with regard to water consumption and water conservation policies adopted during the drought of 1977. The proposed model, which takes precipitation, temperature, month effects, and uniqueness of communities into account, is considered superior to the use of simple percent changes in evaluating the significance of reduction in water consumption due to conservation efforts. Among the variety of policies reviewed, mandatory policies with per capita restrictions were found to be most effective. However, voluntary conservation policies also resulted in significant reduction in some communities located in close proximity to other communities with severer water shortages. High credibility of local governments as to water shortage information was singled out as the most important factor in successful conservation by water resource policymakers of these communities. Use of the proposed predictive model for water consumption was demonstrated. Recommendations regarding future water conservation were made.     

Trace-metal detoxification in the ventral caeca of Orchestia gammarellus (Crustacea: Amphipoda)

The talitrid amphipod crustacean Orchestia gammarellus (Pallas) was collected from metal-contaminated (Dulas Bay, Gironde) and control (Millport) sites in the UK and France. Irrespective of site of origin, the amphipods showed the same physiological mechanism of trace-metal detoxification, involving the ventral caeca. Copper was always present in lysosomal residual bodies in the ventral caeca. Following laboratory exposure to zinc and cadmium, the lysosomes usually contained both copper and zinc but cadmium was not detectable. The lysosomal copper concentration is positively correlated to that of sulphur, while the concentration of lysosomal zinc is related to that of phosphorus. Results are interpreted in terms of the differential rates of turnover of metallothioneins chelating copper, zinc or cadmium. Received: 9 February 1999 / Accepted: 2 December 1999     

Process design for the environment: A multi-objective framework under uncertainty

Designing chemical processes for the environment requires consideration of several indexes of environmental impact including ozone depletion, global warming potentials, human and aquatic toxicity, photochemical oxidation, and acid rain potentials. Current methodologies, such as the generalized waste reduction algorithm (WAR), provide a first step towards evaluating these impacts. However, to address the issues of accuracy and the relative weights of these impact indexes, one must consider the problem of uncertainties. Environmental impacts must also be weighted and balanced against other concerns, such as their cost and long-term sustainability. These multiple, often conflicting, goals pose a challenging and complex optimization problem, requiring multi-objective optimization under uncertainty. This paper will address the problem of quantifying and analyzing the various objectives involved in process design for the environment. Towards this goal, we proposed a novel multi-objective optimization framework under uncertainty. This framework is based on new and efficient algorithms for multi-objective optimization and for uncertainty analysis. This approach finds a set of potentially optimal designs where trade-offs can be explicitly identified, unlike cost-benefit analysis, which deals with multiple objectives by identifying a single fundamental objective and then converting all the other objectives into this single currency. A benchmark process for hydrodealkylation (HDA) of toluene to produce benzene modeled in the ASPEN simulator is used to illustrate the usefulness of the approach in finding environmentally friendly and cost-effective designs under uncertainty.