| 000 | 07535cas|a2200457 i 4500 | ||
|---|---|---|---|
| 005 | 20260818084522.0 | ||
| 008 | 181197s1991 th uzm rtt 00| aseng d | ||
| 035 | _a.b10156318 | ||
| 099 | 9 | _aAIT Diss. no. EV-91-2 | |
| 100 | 1 | _aDharmappa, Hagare Bhimappa | |
| 245 | 1 | 0 | _aComputer aided optimization and design of water treatment systems incorporating particle size distribution |
| 260 |
_aBangkok : _bAsian Institute of Technology, _c1991 |
||
| 300 | _a207 p. | ||
| 490 | 1 |
_aDissertation ; _vno. EV-91-2 |
|
| 500 | _aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering, School of Environment, Resources and Development | ||
| 502 | _aThesis (Ph.D.) - Asian Institute of Technology, 1991 | ||
| 520 | _aOptimization models are required for the optimal design of water and wastewater treatment systems. Although many optimization models have been developed for wastewater treatment systems, very few models are available for water treatment systems. However, these works substantially demonstrate the scope for optimization in identifying the economically and technically feasible alternative designs. The present study is aimed at providing a tool towards this end, which is unique compared to the past works, as it includes: {u2022} Integration of process optimization in system optimization {u2022} Particle size distribution (PSD} for process design and selection {u2022} Membrane processes in the alternative evaluation {u2022} Sludge treatment/handling processes {u2022} Multi-criteria for alternatives evaluation {u2022} Provision for easy incorporation of laboratory/pilot scale data Overall water treatment system design requires determination of: system configuration, level of operation of each process, and individual process design and operating parameters. This can be achieved only through the integration of process optimization in the system optimization, which provides an added advantage of using two separate algorithms. The process optimization, which mostly involves continuous decision variables, can be tactfully handled with some sophisticated optimization tools like nonlinear programming (NLP) or quadratic programming (QP) techniques. On the other hand the system optimization, where the decision has to be made between processes, involves discrete decision variables (and comparatively less number of variables) which can be conveniently handled with simple optimization techniques like enumeration. Recently, many researchers have pointed out the shortcomings of using nonspecific parameters like TU, SS, etc., in the design and selection of processes. They have demonstrated the effect of PSD on the performance of the individual processes, implying that the incorporation of PSD could lead to optimal process design and selection. High and stable water quality, little or no use of disinfectant, compact treatment units, etc . , justify the inclusion of membrane processes in alternatives evaluation. Increasing awareness against environmental pollution and stringent regulations on the handling and disposal of water treatment sludge necessitates the consideration of sludge treatment in the overall system design. Besides cost, other criteria like energy, land, sludge production, etc., often become important in the evaluation of various alternatives. Further, all the optimization techniques rely heavily on the mathematical descriptions of the process/ system which are complicated to formulate. Mathematical models alone cannot fully describe the processes. It is necessary to (iii) supplement the data through laboratory/pilot scale study. Therefore provision should be made for easy incorporation of such data. The principal objective of the present research was to demonstrate the importance of incorporating all the above five features in the overall optimization of water treatment systems consisting of six water (rapid mixing, flocculation, sedimentation, granular filtration, and crossflow microfiltration and ultrafiltration) and thirteen sludge treatment processes. The review of literature revealed the lack of process models and cost functions for certain processes. Thus the objectives also include: {u2022} Development of a new model for crossflow filtration incorporating polydispersity of the influent {u2022} Development of cost functions for crossflow filtration {u2022} Modification of granular filtration cost functions to reflect some of the decision variables The software was developed for system simulation/ optimization, process simulation/optimization, and model parameter estimation. The cost functions developed for crossflow filtration were from the data supplied by a French and Danish company, while the cost data for all other processes were obtained for the USA conditions from the literature . Results of the process optimization demonstrated that the design of a granular filter and flocculator is highly sensitive to particle size distribution in the influent. The cost of a granular filter to treat the influents with the same total quantity of particles but with different influent PSD to the same degree was found to vary between 12-46%. The cost was about 46% higher for the influent with finer particles than with coarser particles. The selection of design and operating parameters was sensitive to influent PSD. In system optimization these cost differences were further amplified yielding 16-64% of difference. The system configurations, level of treatment, and design and operating parameters were different for each influent PSD. Further, for a small water treatment system it was found that the crossflow microfiltration {CFMF) is an economical alternative for some influents, which are difficult to treat with usual treatment system comprising granular filtration. This study is a first step towards more practical oriented optimization modeling of water treatment systems. In this field there is a lot of scope for further research and many potential areas have been discussed. | ||
| 650 | 0 |
_aWater _xPurification _xMathematical models |
|
| 650 | 0 | _aMathematical optimization | |
| 700 | 1 |
_aVerink, Johan, _eChairperson |
|
| 700 |
_aVigneswaran, Saravanamuthu, _eExamination Committee |
||
| 700 | 1 |
_aFujiwara, Okitsugu, _eExamination Committee |
|
| 700 | 0 |
_aChongrak Polprasert, _eExamination Committee |
|
| 700 | 1 |
_aSchroder, Hans, _eExamination Committee |
|
| 700 | 1 |
_aTebbutt, T.H.Y., _eExamination Committee |
|
| 710 | 2 |
_aJapan Government, _eScholarship Donor |
|
| 810 | 2 |
_aAsian Institute of Technology. _tDissertation ; _vno. EV-91-2 |
|
| 856 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B17425 |
||
| 907 |
_a.b10156318 _bmnait _cy |
||
| 902 | _a240411 | ||
| 998 |
_b2 _c951012 _dm _ea _fy _g0 |
||
| 945 | _lmnait | ||
| 945 | _lmnait | ||
| 945 | _lmnarc | ||
| 942 | _c20 | ||
| 942 | _c40 | ||
| 909 |
_aBarcode : 30050003058228 _bCREATED : 1995-10-13 _cRECORD # : i10184776 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 1 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 909 |
_aBarcode : 30050003058236 _bCREATED : 1995-10-13 _cRECORD # : i10184788 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 1 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 909 |
_aBarcode : 30050160005004 _bCREATED : 2016-01-02 _cRECORD # : i12876069 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 999 |
_c20786 _d20786 |
||