Before you read this you should look at;
//www.diynot.com/forums/building/multi-fuel-wood-burner-install-hell.339509/#2533135
Firstly I would like to thanks every for there input on this issue above and the thread at http://www.greenbuildingforum.co.uk/newforum/comments.php?DiscussionID=2937 was most interesting.
But the one thing that is absolutely clear to me is that the decision to line or not, should NOT be in hands of the installer. Simply because he or she is human and therefore not dispassionate about the situation.
Also the decision is highly circumstantial inasmuch as an inexperienced installer might air on the side of caution and always line when it is completely unnecessary. And a highly experienced installer who rarely lines a chimney may operate in an inner city warmer part of the country and many do mainly terraced houses were flue condensation is rarely an issue. Also I would imagine there is the influence of profit and difficulty of the installation. Inasmuch as “there is not much work on at the moment” (middle of June) so I’ll recommend it needs lining. Or, that three story house has really difficult chimney access and I’m up to my eyes in work in January and its wet, windy and cold so I’ll just go for a quick and warm inside install.
What I recommended is as follows:
(1) Installers should forward to HETAS the details of every installation they can recall and or have records of. This may require a revisit and should include as much detail as possible about the installation. It should I guess included information like chimney dimensions, internal/external, facing direction, postcode, lined/unlined etc etc. Also and most importantly the customers name and address. (This should also be an on going process whenever new installs are requested and completed. See 3)
(2) HETAS should then send a questioner to every client that will contain questions that will evaluate the performance of the installation and if they had had any major problems like fire etc. HETAS should then build a database of this data and from this data an Artificial Neural Network should be trained.
(3) When a client requests a Stove installation, the installer would visit the site take a set of site measurements that are compatible with HETAS data set (item 1) The installer would then entre this data into the HETAS web site were the Artificial Neural Network would cross reference the installation data with the customers experience data and product a simple Yes or No answer. The output for the Artificial Neural Network could easily be skewed to air on the side on caution and therefore recommend lining is the result is marginal.
This would removed the element of profit driven installs, take the emotion out of installer decisions and put some real customer and environment feedback into the process. This would also be a feather in the cap of HETAS as they could show that they are offering both customers and installers a Yes or No decision that is based on science, empirical data and state of the art technology.
Reading on ANN
http://en.wikipedia.org/wiki/Artificial_neural_network
//www.diynot.com/forums/building/multi-fuel-wood-burner-install-hell.339509/#2533135
Firstly I would like to thanks every for there input on this issue above and the thread at http://www.greenbuildingforum.co.uk/newforum/comments.php?DiscussionID=2937 was most interesting.
But the one thing that is absolutely clear to me is that the decision to line or not, should NOT be in hands of the installer. Simply because he or she is human and therefore not dispassionate about the situation.
Also the decision is highly circumstantial inasmuch as an inexperienced installer might air on the side of caution and always line when it is completely unnecessary. And a highly experienced installer who rarely lines a chimney may operate in an inner city warmer part of the country and many do mainly terraced houses were flue condensation is rarely an issue. Also I would imagine there is the influence of profit and difficulty of the installation. Inasmuch as “there is not much work on at the moment” (middle of June) so I’ll recommend it needs lining. Or, that three story house has really difficult chimney access and I’m up to my eyes in work in January and its wet, windy and cold so I’ll just go for a quick and warm inside install.
What I recommended is as follows:
(1) Installers should forward to HETAS the details of every installation they can recall and or have records of. This may require a revisit and should include as much detail as possible about the installation. It should I guess included information like chimney dimensions, internal/external, facing direction, postcode, lined/unlined etc etc. Also and most importantly the customers name and address. (This should also be an on going process whenever new installs are requested and completed. See 3)
(2) HETAS should then send a questioner to every client that will contain questions that will evaluate the performance of the installation and if they had had any major problems like fire etc. HETAS should then build a database of this data and from this data an Artificial Neural Network should be trained.
(3) When a client requests a Stove installation, the installer would visit the site take a set of site measurements that are compatible with HETAS data set (item 1) The installer would then entre this data into the HETAS web site were the Artificial Neural Network would cross reference the installation data with the customers experience data and product a simple Yes or No answer. The output for the Artificial Neural Network could easily be skewed to air on the side on caution and therefore recommend lining is the result is marginal.
This would removed the element of profit driven installs, take the emotion out of installer decisions and put some real customer and environment feedback into the process. This would also be a feather in the cap of HETAS as they could show that they are offering both customers and installers a Yes or No decision that is based on science, empirical data and state of the art technology.
Reading on ANN
http://en.wikipedia.org/wiki/Artificial_neural_network
