{"id":1016,"date":"2016-03-24T12:37:58","date_gmt":"2016-03-24T04:37:58","guid":{"rendered":"http:\/\/www.ukm.my\/jkukm\/?page_id=1016"},"modified":"2016-06-07T10:35:59","modified_gmt":"2016-06-07T02:35:59","slug":"volume-11-1999","status":"publish","type":"page","link":"https:\/\/www.ukm.my\/jkukm\/volume-11-1999\/","title":{"rendered":"Volume 11(2) (1999)"},"content":{"rendered":"

Table of Contents<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
No.<\/strong><\/td>\nArticle<\/strong><\/td>\n\n

Detail<\/strong><\/p>\n<\/td>\n<\/tr>\n

1.<\/td>\nSintering Characteristics and Microstructures of Colloidally
\nAndanastuti Muchtar<\/sup><\/span><\/span>Abstract<\/span>
High-purity (99.99 %) fine-grained alumina samples were prepared using the\u00a0colloidal processing method to minimise the formation of agglomerates\u00a0during powder consolidation. In this research, the soft agglomerates were\u00a0broken down by ultrasonic agitation whereas sedimentation at pH ~ 2.0\u00a0successfully stabilised the suspension and separated it from the unwanted\u00a0hard agglomerates. Consolidation was achieved through slip casting directly from the suspension to avoid the reflocculation of the powders. The removal of\u00a0the agglomerates from the initial starting material resulted in the increase of\u00a0packing density during compaction and led to a higher green density A dense\u00a0homogeneous microstructure was thus achieved with a uniform, fine grain size\u00a0generated at low sintering temperatures. Submicron-grained alumina of about\u00a00.5 u<\/em>m in grain size was produced at the lowest sintering temperature of\u00a01310\u00b0C.<\/div><\/td>\n
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\"download<\/a>Page 3-12<\/span><\/p>\n<\/td>\n<\/tr>\n

2.<\/td>\nProses Pengisaran Bijiran Makanan (Grinding Process of Food Materials)
\n<\/em>Siti Masrinda Taisirin & Too Soo Ping<\/sup><\/span><\/span>Abstract<\/span>
The effects of time and total loading on the grinding process of food materials\u00a0are presented in this work. Results show that the efficiency of process: defined\u00a0as the weight ratio of product produced over the feed material, increases as the\u00a0time of grinding increases (product in this case is defined as particles with size less than 250 1 u<\/em>m). At any given time, a volume fraction of 45% of the total volume of the grinding machine is found to be the optimum loading to\u00a0produced the highest amount of product. me work index (Bond 1951) and\u00a0attrition index (ASTM D440) of the food materials were calculated and found to\u00a0be related in inverse manner.<\/div><\/td>\n
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\"download<\/a>Page 13-21<\/span><\/p>\n<\/td>\n<\/tr>\n

3.<\/td>\nSistem Pakar Untuk Pembelajaran Kualiti Kuasa (Intelligent System for Power Quality Learning)
\n<\/em>Azah Mohamed & Mohamad Husaini Hashim<\/sup><\/span><\/span>Abstract<\/span>
This paper presents the use of artificial intelligence for the purpose of learning\u00a0power quality. A software package has been developed in the form of an object\u00a0oriented expert system by using the KAPPA-PC software. The power quality\u00a0problem is arranged by means of information rules such as types of power\u00a0disturbances, symptoms, causes and their solutions. The rule based reasoning\u00a0process is by means of backward chaining. In the objective oriented approach,\u00a0the power disturbances are represented as. classes and objects and the\u00a0methods are used for communication between the objects. The developed\u00a0expert system is also made user-friendly for the purpose of power quality\u00a0education. It is shown that the proposed expert system can significantly reduce\u00a0design and implementation efforts.<\/div><\/td>\n
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\"download<\/a>Page 23-30<\/span><\/p>\n<\/td>\n<\/tr>\n

4.<\/td>\nRanking of Intangible Process Selection Criteria for Biological Wastewater Treatment System
\nAyub Md. Som<\/sup><\/span><\/span>Abstract<\/span>
A process selection methodology for an industrial waste water treatment was\u00a0developed and later incorporated into an expert system shell (XpertRule)\u00a0which allowed a selection to be made based on biological treatment process\u00a0alternatives. The methodology deals not only with tangible parameters but\u00a0also intangible or non-numerical parameters which need further\u00a0quantification on a hierarchical manner. This paper discusses the procedure\u00a0for ranking the intangible parameter by incorporating statistical elements.\u00a0Principal Component Analysis (PCA) for biological waste water treatment\u00a0process selection. The quantification of intangible non numeric data has been\u00a0rationalised in the study. This has overcome the problem of subjective\u00a0judgement given to the process selection criteria as encountered by previous\u00a0workers.<\/div><\/td>\n
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\"download<\/a>Page 31-49<\/span><\/p>\n<\/td>\n<\/tr>\n

5.<\/td>\nDevelopment of Process Selection Model for Industrial Wastewater Treatment Using an Expert System
\nAyub Md. Som<\/sup><\/span><\/span>Abstract<\/span>
A process selection methodology was developed for an industrial wastewater\u00a0treatment plant. The starting point of the procedure was the categorisation of\u00a0tho wastewater based on industry type, general pollution indicators. or\u00a0contaminant removal processes giving a standardised compositional code\u00a0which consisted of seven basic wastewater characteristics. A preliminary design\u00a0assessment was undertaken by comparing the effluent parameters with the\u00a0desired effluent quality or consent conditions; where an effluent parameter\u00a0was not known, a minimum characterisation set of analysis data was used. A\u00a0preliminary process selection was carried out in terms of maximum volumetric\u00a0loading rates and depths for each process which gave the required footprint\u00a0area for the reactor. In addition to the reactor. the overall land area\u00a0requirement was determined by incorporating the potential ancillary\u00a0equipment such as sedimentation and sludge processing tanks for each\u00a0process. The process selection was further refined by the establishment of\u00a0performance graphs for each process. based on the volumetric loading rates\u00a0and the percentage removal of COD or BOD. Based on these graphs. each\u00a0process Can be quantified as to whether the COD or BOD consent is met in\u00a0relation 10 the respective volumetric loading rate. If there was not a\u00a0performance envelope available for the process. data was sought from a treatability study in the laboratory. The methodology has been\u00a0incorporated into an expert system shell (XpertRule), which runs on a PC and\u00a0provides a simple user interface. Certain provisions are made available in the\u00a0program for new information to be added into the knowledge base. The automation of the methodology currently allows the user to make a selection\u00a0based on biological treatment process alternatives.<\/div><\/td>\n
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\"download<\/a>Page 51-83<\/span><\/p>\n<\/td>\n<\/tr>\n

6.<\/td>\nRemoval of Mixed Heavy Metals by Hyroxide Precipitation
\nSiti Rozaimah Sheikh Abdullah, Rakmi Abd. Rahman, Abu Bakar Mohamad, Mohd Marzuki Mustafa & Abdul Amir Hassna Khadum<\/sup><\/span><\/span>Abstract<\/span>
Heavy metals such as chromium, nickel, copper. zinc and lead, can be\u00a0effectively removed from metal finishing wastewaters by hydroxide\u00a0precipitation. Prior to precipitation, ferrous sulphate is added to reduce\u00a0chromium from hexavalent to trivalent state and also to form stable complex\u00a0ferrocyanides with cyanide. This batch study was carried out to evaluate the\u00a0minimum pH range for the minimum solubility of metal hydroxide\u00a0precipitation. All the metals (Cu. Ni. Zn. Pb and Cr) were present\u00a0simultaneously in synthetic wastewaters. In the absence of cyanide, the\u00a0maximum hydroxide precipitation for Cu. Ni. Zn. Pb and Cr occurred at pH\u00a0ranges of 6.5-12. 9-12. 8.5-12. 8.5-12 and 8-12 respectively. When cyanide was\u00a0added into the wastewater. only pH range of minimum solubility for Cu was\u00a0shifted to pH 8.5-11. For other metals, their pH ranges were not affected by the\u00a0presence of cyanide. In addition, stable complex ferrocyanides precipitated\u00a0optimally starting at pH 9.<\/div><\/td>\n
\u00a0 \u00a0\"download<\/a>Page 85-101<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Table of Contents No. Article Detail 1. Sintering Characteristics and Microstructures of Colloidally Andanastuti Muchtar Page 3-12 2. Proses Pengisaran Bijiran Makanan (Grinding Process of …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-full-width.php","meta":{"footnotes":""},"folder":[],"class_list":["post-1016","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/pages\/1016","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/comments?post=1016"}],"version-history":[{"count":27,"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/pages\/1016\/revisions"}],"predecessor-version":[{"id":1983,"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/pages\/1016\/revisions\/1983"}],"wp:attachment":[{"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/media?parent=1016"}],"wp:term":[{"taxonomy":"folder","embeddable":true,"href":"https:\/\/www.ukm.my\/jkukm\/wp-json\/wp\/v2\/folder?post=1016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}